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In this brief, stabilization of Boolean networks (BNs) by flipping a subset of nodes is considered, here we call such action state-flipped control. The state-flipped control implies that the logical variables of certain nodes are flipped from 1 to 0 or 0 to 1 as time flows. Under state-flipped control on certain nodes, a state-flipped-transition matrix is defined to describe the impact on the state transition space. Weak stabilization is first defined and then some criteria are presented to judge the same. An algorithm is proposed to find a stabilizing kernel such that BNs can achieve weak stabilization to the desired state with in-degree more than 0. By defining a reachable set, another approach is proposed to verify weak stabilization, and an algorithm is given to obtain a flip sequence steering an initial state to a given target state. Subsequently, the issue of finding flip sequences to steer BNs from weak stabilization to global stabilization is addressed. In addition, a model-free reinforcement algorithm, namely the Q-learning (QL) algorithm, is developed to find flip sequences to achieve global stabilization. Finally, several numerical examples are given to illustrate the obtained theoretical results.

With the development of Earth observation technology, very-high-resolution (VHR) image has become an important data source of change detection. Nowadays, deep learning methods have achieved conspicuous performance in the change detection of VHR images. Nonetheless, most of the existing change detection models based on deep learning require annotated training samples. In this paper, a novel unsupervised model called kernel principal component analysis (KPCA) convolution is proposed for extracting representative features from multi-temporal VHR images. Based on the KPCA convolution, an unsupervised deep siamese KPCA convolutional mapping network (KPCA-MNet) is designed for binary and multi-class change detection. In the KPCA-MNet, the high-level spatial-spectral feature maps are extracted by a deep siamese network consisting of weight-shared PCA convolution layers. Then, the change information in the feature difference map is mapped into a 2-D polar domain. Finally, the change detection results are generated by threshold segmentation and clustering algorithms. All procedures of KPCA-MNet does not require labeled data. The theoretical analysis and experimental results demonstrate the validity, robustness, and potential of the proposed method in two binary change detection data sets and one multi-class change detection data set.

Spherical images or videos, as typical non-Euclidean data, are usually stored in the form of 2D panoramas obtained through an equirectangular projection, which is neither equal area nor conformal. The distortion caused by the projection limits the performance of vanilla Deep Neural Networks (DNNs) designed for traditional Euclidean data. In this paper, we design a novel Spherical Deep Neural Network (DNN) to deal with the distortion caused by the equirectangular projection. Specifically, we customize a set of components, including a spherical convolution, a spherical pooling, a spherical ConvLSTM cell and a spherical MSE loss, as the replacements of their counterparts in vanilla DNNs for spherical data. The core idea is to change the identical behavior of the conventional operations in vanilla DNNs across different feature patches so that they will be adjusted to the distortion caused by the variance of sampling rate among different feature patches. We demonstrate the effectiveness of our Spherical DNNs for saliency detection and gaze estimation in 360° videos. To facilitate the study of the 360 video saliency detection, we further construct a large-scale 360° video saliency detection dataset. Comprehensive experiments validate the effectiveness of our proposed Spherical DNNs for spherical handwritten digit classification and sport classification, saliency detection and gaze tracking in 360° videos.

Varying density of point clouds increases the difficulty of 3D detection. In this paper, we present a context-aware dynamic network (CADNet) to capture the variance of density by considering both point context and semantic context. Point-level contexts are generated from original point clouds to enlarge the effective receptive filed. They are extracted around the voxelized pillars based on our extended voxelization method and processed with the context encoder in parallel with the pillar features. With a large perception range, we are able to capture the variance of features for potential objects and generate attentive spatial guidance to help adjust the strengths for different regions. In the region proposal network, considering the limited representation ability of traditional convolution where same kernels are shared among different samples and positions, we propose a decomposable dynamic convolutional layer to adapt to the variance of input features by learning from the local semantic context. It adaptively generates the position-dependent coefficients for multiple fixed kernels and combines them to convolve with local features. Based on our dynamic convolution, we design a dual-path convolution block to further improve the representation ability. We conduct experiments on KITTI dataset and the proposed CADNet has achieved superior performance of 3D detection outperforming SECOND and PointPillars by a large margin at the speed of 30 FPS.

Cross-manifold clustering is a hard topic and many traditional clustering methods fail because of the cross-manifold structures. In this paper, we propose a Multiple Flat Projections Clustering (MFPC) to deal with cross-manifold clustering problems. In our MFPC, the given samples are projected into multiple subspaces to discover the global structures of the implicit manifolds. Thus, the cross-manifold clusters are distinguished from the various projections. Further, our MFPC is extended to nonlinear manifold clustering via kernel tricks to deal with more complex cross-manifold clustering. A series of non-convex matrix optimization problems in MFPC are solved by a proposed recursive algorithm. The synthetic tests show that our MFPC works on the cross-manifold structures well. Moreover, experimental results on the benchmark datasets show the excellent performance of our MFPC compared with some state-of-the-art clustering methods., Comment: 12 pages, 58 figures

This paper attempts at improving the accuracy of Human Action Recognition (HAR) by fusion of depth and inertial sensor data. Firstly, we transform the depth data into Sequential Front view Images(SFI) and fine-tune the pre-trained AlexNet on these images. Then, inertial data is converted into Signal Images (SI) and another convolutional neural network (CNN) is trained on these images. Finally, learned features are extracted from both CNN, fused together to make a shared feature layer, and these features are fed to the classifier. We experiment with two classifiers, namely Support Vector Machines (SVM) and softmax classifier and compare their performances. The recognition accuracies of each modality, depth data alone and sensor data alone are also calculated and compared with fusion based accuracies to highlight the fact that fusion of modalities yields better results than individual modalities. Experimental results on UTD-MHAD and Kinect 2D datasets show that proposed method achieves state of the art results when compared to other recently proposed visual-inertial action recognition methods.

Underwater captured images are usually degraded by low contrast, hazy, and blurry due to absorbing and scattering, which limits their analyses and applications. To address these problems, a red channel prior guided variational framework is proposed based on the complete underwater image formation model (UIFM). Unlike most of the existing methods that only consider the direct transmission and backscattering components, we additionally include forward scattering component into the UIFM. In the proposed variational framework, we successfully incorporate the normalized total variation item and sparse prior knowledge of blur kernel together. In addition, we perform the estimation of blur kernel by varying image resolution in a coarse-to-fine manner to avoid local minima. Moreover, for solving the generated non-smooth optimization problem, we employ the alternating direction method of multipliers (ADMM) to accelerate the whole progress. Experimental results demonstrate that the proposed method has a good performance on dehazing and deblurring. Extensive qualitative and quantitative comparisons further validate its superiority against the other state-of-the-art algorithms. The code is available online at: https://github.com/Hou-Guojia.

Diabetic Foot Ulcer (DFU) is a complication of diabetes that causes lower limb amputation. In this work, a unique stacked parallel convolution layers-based network (DFU_SPNet) is proposed to perform DFU vs. normal skin classification. The main objective of this work is to design an effective CNN-based classification model, along with proper fine-tuning of optimizer settings. DFU_SPNet consists of 3 blocks of parallel convolution layers with multiple kernel sizes, for local and global feature abstractions. The proposed DFU_SPNet, trained using SGD (with momentum) optimizer with 1 e − 2 learning rate on the DFUNet dataset, outperformed the current state-of-the-art results with an AUC of 0.974.

Geostatistical modeling, one of the prime motivating applications for exascale computing, is a technique for predicting desired quantities from geographically distributed data, based on statistical models and optimization of parameters. Spatial data are assumed to possess properties of stationarity or non-stationarity via a kernel fitted to a covariance matrix. A primary workhorse of stationary spatial statistics is Gaussian maximum log-likelihood estimation (MLE), whose central data structure is a dense, symmetric positive definite covariance matrix of the dimension of the number of correlated observations. Two essential operations in MLE are the application of the inverse and evaluation of the determinant of the covariance matrix. These can be rendered through the Cholesky decomposition and triangular solution. In this contribution, we reduce the precision of weakly correlated locations to single- or half- precision based on distance. We thus exploit mathematical structure to migrate MLE to a three-precision approximation that takes advantage of contemporary architectures offering BLAS3-like operations in a single instruction that are extremely fast for reduced precision. We illustrate application-expected accuracy worthy of double-precision from a majority half-precision computation, in a context where uniform single-precision is by itself insufficient. In tackling the complexity and imbalance caused by the mixing of three precisions, we deploy the PaRSEC runtime system. PaRSEC delivers on-demand casting of precisions while orchestrating tasks and data movement in a multi-GPU distributed-memory environment within a tile-based Cholesky factorization. Application-expected accuracy is maintained while achieving up to $1.59X$ 1 . 59 X by mixing FP64/FP32 operations on 1536 nodes of HAWK or 4096 nodes of Shaheen II , and up to $2.64X$ 2 . 64 X by mixing FP64/FP32/FP16 operations on 128 nodes of Summit , relative to FP64-only operations. This translates into up to 4.5, 4.7, and 9.1 (mixed) PFlop/s sustained performance, respectively, demonstrating a synergistic combination of exascale architecture, dynamic runtime software, and algorithmic adaptation applied to challenging environmental problems.

We propose a novel pairwise distance measure between image keypoint sets, for the purpose of large-scale medical image indexing. Our measure generalizes the Jaccard index to account for soft set equivalence (SSE) between keypoint elements, via an adaptive kernel framework modeling uncertainty in keypoint appearance and geometry. A new kernel is proposed to quantify the variability of keypoint geometry in location and scale. Our distance measure may be estimated between $O(N^2)$ image pairs in $O(N~\log~N)$ operations via keypoint indexing. Experiments report the first results for the task of predicting family relationships from medical images, using 1010 T1-weighted MRI brain volumes of 434 families including monozygotic and dizygotic twins, siblings and half-siblings sharing 100%-25% of their polymorphic genes. Soft set equivalence and the keypoint geometry kernel improve upon standard hard set equivalence (HSE) and appearance kernels alone in predicting family relationships. Monozygotic twin identification is near 100%, and three subjects with uncertain genotyping are automatically paired with their self-reported families, the first reported practical application of image-based family identification. Our distance measure can also be used to predict group categories, sex is predicted with an AUC=0.97. Software is provided for efficient fine-grained curation of large, generic image datasets., 10 pages, 8 figures

Existing convolution techniques in artificial neural networks suffer from huge computation complexity, while the biological neural network works in a much more powerful yet efficient way. Inspired by the biological plasticity of dendritic topology and synaptic strength, our method, Learnable Heterogeneous Convolution, realizes joint learning of kernel shape and weights, which unifies existing handcrafted convolution techniques in a data-driven way. A model based on our method can converge with structural sparse weights and then be accelerated by devices of high parallelism. In the experiments, our method either reduces VGG16/19 and ResNet34/50 computation by nearly 5x on CIFAR10 and 2x on ImageNet without harming the performance, where the weights are compressed by 10x and 4x respectively; or improves the accuracy by up to 1.0% on CIFAR10 and 0.5% on ImageNet with slightly higher efficiency. The code will be available on www.github.com/Genera1Z/LearnableHeterogeneousConvolution., Published in Neural Networks journal

The binarized neural network (BNN) inference accelerators show great promise in cost-and power-restricted domains. However, the performances of these accelerators are still severely limited by the significant redundancies in BNNs inference. In this brief, we introduce channel-aware sparse accelerator (CAA) to alleviate the performance degradations induced by the redundancies in BNNs while maintaining original accuracies. First, motivated by the observation that the convolution processes of our rebuilt rectangle kernels contain many redundant operations which can be skipped by exploiting the BNN-specific property, we convert the entire original XNOR-popcount convolutions of each neuron into channel-aware-popcount (CAP) operations for all binarized convolutional and fully-connected layers in CAA by employing rectangle kernel simplification strategy and eliminate the unnecessary operations. Meanwhile, these CAP operations can be implemented to directly gain the final output without any extra steps. Furthermore, inspired by our new observations on two specific kinds of properties of the CAP operations, we adopt group pruning approach to save the remaining redundant CAP operations. Experimental results show that our design evaluated on an embedded FPGA achieves 4.2-6.6× inference-speedup, 3.6-5.5× energy-efficiency enhancement, and 1.35× resource-efficiency improvement compared with state-of-the-art works.

This brief presents an event-driven spiking multi-kernel convolution architecture for processing address-event representation (AER) streams from dynamic vision sensor (DVS) chips. The processor architecture is designed based on leaky integrate-and-fire (LIF) neural model, and employs pipeline scheme to accelerate data processing. A new scheme for arranging neuron membrane potentials and kernels in memories is proposed, which enables row-by-row kernel processing and accelerates the multi-kernel convolution computation. An FPGA prototype of the proposed architecture is implemented on a Xilinx Zynq FPGA development board with 100 MHz clock frequency. The proposed processor architecture computes 64 filters with configurable kernel size (from 1x1 to 32x32) on input flow, obtaining the latency of 0.10 us to 10.33 us for convoluting an event, and the energy of 0.12 nJ and 12.08 nJ per event per convolution, respectively.

Fine-grained recognition poses the challenge of discriminating categories with only small subtle visual differences, which can be easily overwhelmed by diverse appearance within categories. Conventional approaches generally locate discriminative parts and then recognize the part-based features. However, we find that tuning the effective receptive field (ERF) of the network to the task plays the key role, which enables significant regions to contribute more to the output. Inspired by the receptive field stimulation mechanism of the visual cortex, we propose a Dynamic Perception framework as a solution. Our framework adapts the ERF by considering the image space and the kernel space simultaneously. In the image space, the Spatial Selective Sampling module is adopted to enlarge informative regions locally. In the kernel space, Spatial Selective Kernel convolution is introduced to adapt different kernel sizes for regions of interest and backgrounds by embedding spatial attention in the multi-path convolution. Extensive experiments on challenging benchmarks, including CUB-200-2011, FGVC-Aircraft, and Stanford Cars, demonstrate that our method yields a performance boost over the state-of-the-art methods.

Filtering and propagation are two basic operations in image analysis and rendering, and they are also widely used in computer graphics and machine learning. However, the models of filtering and propagation were based on diverse mathematical formulations, which have not been fully understood. This article aims to explore the properties of both filtering and propagation models from a partial differential equation (PDE) learning perspective. We propose a unified PDE learning framework based on nonlinear reaction-diffusion with a guided map, graph Laplacian, and reaction weight. It reveals that: 1) the guided map and reaction weight determines whether the PDE produces filtering or propagation diffusion and 2) the kernel of graph Laplacian controls the diffusion pattern. Based on the proposed PDE framework, we derive the mathematical relations between different models, including learning to diffusion (LTD) model, label propagation, edit propagation, and edge-aware filter. In practical verification, we apply the PDE framework to design diffusion operations with the adaptive kernel to tackle the ill-posed problem of facial intrinsic image analysis (FIIA). A flexible task-aware FIIA system is built to achieve various facial rendering effects, such as face image relighting and delighting, artistic illumination transfer, illumination-aware face swapping, or transfiguring. Qualitative and quantitative experiments show the effectiveness and flexibility of task-aware FIIA and provide new insights on PDE learning for visual analysis and rendering.

This paper considers a variational model for single image blind restoration. By exploiting the fractional-order total variation (FTV) and Lp quasinorm relaxation, a non-convex fractional-order variational model is proposed to restore the image blurred by an unknown blur kernel. A quaternion FTV model is first put forward to exploring more directional information of an image. The new model utilizes non-convex and non-smooth quaternion FTV with Lp quasinorm to constrain the image and L1-norm to constrain the blur kernel, which are unified to a unified regularization framework. Further, an efficient algorithm is proposed to solve the non-convex problem via using the alternating direction minimization. The extensive experiments demonstrate the efficiency and viability of the proposed method and reveal superior performances in comparison with several existing methods.

We build up a decomposition for the flow generated by the heat equation with a real analytic memory kernel. It consists of three components: The first one is of parabolic nature; the second one gathers the hyperbolic component of the dynamics, with null velocity of propagation; the last one exhibits a finite smoothing effect. This decomposition reveals the hybrid parabolic-hyperbolic nature of the flow and clearly illustrates the significant impact of the memory term on the parabolic behavior of the system in the absence of memory terms.

Omni-directional images are becoming more prevalent for understanding the scene of all directions around a camera, as they provide a much wider field-of-view (FoV) compared to conventional images. In this work, we present a novel approach to represent omni-directional images and suggest how to apply CNNs on the proposed image representation. The proposed image representation method utilizes a spherical polyhedron to reduce distortion introduced inevitably when sampling pixels on a non-Euclidean spherical surface around the camera center. To apply convolution operation on our representation of images, we stack the neighboring pixels on top of each pixel and multiply with trainable parameters. This approach enables us to apply the same CNN architectures used in conventional Euclidean 2D images on our proposed method in a straightforward manner. Compared to the previous work, we additionally compare different designs of kernels that can be applied to our proposed method. We also show that our method outperforms in monocular depth estimation task compared to other state-of-the-art representation methods of omni-directional images. In addition, we propose a novel method to fit bounding ellipses of arbitrary orientation using object detection networks and apply it to an omni-directional real-world human detection dataset.

The improved particle swarm optimization algorithm is integrated with variational mode decomposition (VMD) to extract the efficient band-limited intrinsic mode function (BLIMF) of the single and combined power quality events (PQEs). The selected BLIMF of the robust VMD (RVMD) and the privileged Fourier magnitude spectrum (FMS) information are fed to the proposed reduced deep convolutional neural network (RDCNN) for the extraction of the most discriminative unsupervised features. The RVMD-FMS-RDCNN method shows minimum feature overlapping compared with RDCNN and RVMD-RDCNN methods. The feature vector is imported to the novel supervised online kernel random vector functional link network (OKRVFLN) for quick and accurate categorization of complex PQEs. The proposed RVMD-FMS-RDCNN-OKRVFLN method produces excellent recognition capability over RDCNN, RVMD-RDCNN, and RVMD-RDCNN-OKRVFLN methods in noise-free and noisy environments. The unique BLIMF selection, clear detection, descriptive feature extraction, higher learning speed, superior classification accuracy, and robust antinoise performances are considerable importance of the proposed RVMD-FMS-RDCNN-OKRVFLN method. Finally, the proposed method architecture is developed and implemented in a very-high-speed ML506 Virtex-5 FPGA to text, examine, and validate the feasibility, performances, and practicability for online monitoring of the PQEs.

Unsupervised domain adaptation (UDA) has achieved unprecedented success in improving the cross-domain robustness of object detection models. However, existing UDA methods largely ignore the instantaneous data distribution and the sampling strategy during model learning, which could deteriorate the feature representation given large domain shift. In this work, we propose a Self-Guided Adaptation (SGA) model, target at aligning feature representation and transferring object detection models across domains while considering the instantaneous alignment difficulty. The core of SGA is to calculate "hardness" factors for sample pairs indicating domain distance in a kernel space. With the hardness factor, the proposed SGA adaptively indicates the importance of samples and assigns them different constrains. Indicated by hardness factors, Self-Guided Progressive Sampling (SPS) is implemented in an "easy-to-hard" way during model adaptation. Using multi-stage convolutional features, SGA is further aggregated to fully align hierarchical representations of detection models. Extensive experiments on commonly used benchmarks show that SGA improves the state-of-the-art methods with significant margins, while demonstrating the effectiveness on large domain shift.

Mathematical modeling of infectious diseases with non-integer order getting attentions from scientists and researchers day by day. It is obvious that classical models in epidemiology can only be described through a fixed order while models in fractional order derivative are of not fixed order. Having non fixed order the fractional derivative becomes more powerful in modeling real life problems. In the recent era, different novel concepts regarding fractional operators such as the exponential decay and the Mittag–Leffler kernel have been introduced which overcome the limitations of the previous fractional order derivatives. These new operators have been found effective in modeling problems arising in science and engineering. A more recent operator in fractional calculus was introduced that is known as fractal-fractional operator. In this study, we consider this novel approach and apply it to an epidemic model of dengue fever and explore their dynamics. We show some important analysis for the dengue epidemic model in the presence of this new operator. The uniqueness and existence results will be shown. We show the simulation results for the considered model with a novel numerical approach which is not yet considered by anyone for such epidemic model. We obtain results for fractal model when fractional order is one and will have fractional solution when fractal order is one and have when both are present. We show that the fractal-fractional approach is much suitable for an epidemic model rather than fractional operator.

Object detection is an essential task in computer vision. Recently, several convolution neural network (CNN)-based detectors have achieved a great success in natural scenes. However, for optical remote sensing images with a large scale of view, lower proportion of foreground target pixels and drastic differences in object scale present considerable challenges. To address these problems, we propose a novel one-stage detector called the full-scale object detection network (FSoD-Net) which consists of proposed multiscale enhancement network (MSE-Net) backbone cascaded with scale-invariant regression layers (SIRLs). First, MSE-Net provides the multiscale description enhancement by integrated the Laplace kernel with fewer parallel multiscale convolution layers. Second, SIRLs contain three different isolated regression branch layers (i.e., corresponding to small, medium, and large scales), which make default discrete scale bounding boxes (bboxes) cover full-scale object information in regression procedure. A novel specific scale joint loss is also designed that uses the softmax function combined with a strong L₁-norm constraint in each regression branch layer. It can further speed up the convergence and improve the classification scores of predicted bboxes. Finally, extensive experiments are carried on challenge data sets of large-scale dataset for object detection in aerial images (DOTA) and object detection in optical remote sensing images (DIOR) which contain multiple instances from different imaging platforms, and these results demonstrate that FSoD-Net can achieve better performance than other state-of-the-art one-stage detectors, and it can reach a mean average precision (mAP) of 75.33% on DOTA and 71.80% mAP on DIOR, respectively. Especially, the average precision (AP) of tiny object detection can improve 10%-20% approximately.

Synthetic aperture radar (SAR), due to its inherent characteristics, will produce speckle noise, which results in the deterioration of image quality, so the removal of speckle in SAR image is very important for the subsequent high-level image processing. In order to balance the relationship between denoising and texture preservation, we propose a multiscale residual dense dual attention network (MRDDANet) for SAR image denoising. This algorithm can effectively suppress the speckle while fully retaining the texture details of the image. In MRDDANet, shallow features are extracted from the noisy images by multiscale modules with different kernel sizes, and then, the extracted shallow features are mapped to the residual dense dual-attention network to obtain the deep features of SAR image. Finally, the final denoising image is generated through global residual learning. MRDDANet has advantages of both multiscale blocks and residual dense dual attention networks. The dense connection can fully extract features in the image, and the dual-channel attention enables MRDDANet to pay more attention to noise information, which is beneficial to remove noise and keep the details of the original image at the same time. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only improves various objective indicators but also shows great advantages in visual effects.

This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are somany research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual-atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRESASPP- Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage. © 2022 Tech Science Press. All rights reserved.

It is well-known that the convolutional neural network (CNN) is an effective method for synthetic aperture radar (SAR) target classification. In the convolutional layer of CNN, convolutional kernels of different sizes can extract different feature information of the target. The small-size kernel can extract the local texture feature information, and the large-size kernel can extract the global contour feature information. Traditional CNN methods usually use fixed-size kernels for convolution, and they generally lose part of the target's feature information, resulting in the inaccurate classification of the SAR targets. This article proposes a novel CNN model based on multikernel-size feature fusion (MKSFF-CNN) for SAR target classification. MKSFF-CNN designs a convolutional methodology with a multichannel parallel topology, it uses convolutional kernels of different sizes to extract the multikernel-size deep features of the SAR target, and then, these features are fused in an optimal way to acquire the lowest loss. Moreover, MKSFF-CNN concatenates the fused features extracted by the convolutional layers of different dimensions to achieve the finest classification. MKSFF-CNN greatly elevates the feature representation completeness of the SAR targets so that more useful feature information can be exploited for SAR target classification. Undoubtedly, MKSFF-CNN can achieve a better classification performance compared with traditional CNN models with a fixed kernel size. The superiority of MKSFF-CNN is validated on the moving and stationary target acquisition and recognition (MSTAR) dataset with the detailed objective and subjective evaluation.

Compressed sensing (CS) demonstrates significant potential to improve image quality in 3-D millimeter-wave imaging compared with conventional matched filtering (MF). However, existing sparsity-driven 3-D imaging algorithms always suffer from large-scale storage, excessive computational cost, and nontrivial tuning of parameters due to the huge-dimensional matrix-vector multiplication in complicated iterative optimization steps. In this article, we present a novel range migration (RM) kernel-based iterative-shrinkage thresholding network, dubbed as RMIST-Net, by combining the traditional model-based CS method and data-driven deep learning method for near-field 3-D millimeter-wave (mmW) sparse imaging. First, the measurement matrices in ISTA optimization steps are replaced by RM kernels, by which matrix-vector multiplication is converted to the Hadamard product. Then, the modified ISTA optimization is unrolled into a deep hierarchical architecture, in which all parameters are learned automatically instead of manually tuned. Subsequently, 1000 pairs of oracle images with randomly distributed targets and their corresponding echoes are simulated to train the network. A well-trained RMIST-Net produces high-quality 3-D images from range-focused echoes. Finally, we experimentally prove that RMIST-Net is capable process 512 x 512 large-scale imaging tasks within 1 s. Besides, we compare RMIST-Net with other state-of-the-art methods in near-field 3-D imaging applications. Both simulations and real-measured experiments demonstrate that RMIST-Net produces impressive reconstruction performance while maintaining high computational speed compared with conventional and sparse imaging algorithms.

Object detection is a fundamental part of the interpretation of remote sensing imagery. The one-stage object detector has been adopted into this field because of its high computational efficiency. However, this detector suffers from the misalignment among predefined anchor, object, and feature extracted by standard convolution kernel both in spatial and scale. It limits the further improvement of performance, especially for the long-narrow and multiscale geospatial objects. In this article, the problem is defined as the feature misalignment problem. To deal with this issue, an efficient feature aligned single-shot detector (ASSD) is proposed, which consists of two modules: a novel pseudo anchor proposal module (PAPM) and a flexible context-based feature alignment module (CFAM). The PAPM replaces the regular anchor group with the proposed core anchor and refines it to get aligned locations. It can tackle the spatial misalignment between anchors and their corresponding objects and alleviate the negative/positive imbalance problem. Then, the CFAM adaptively adjusts the sampling points of the convolution kernel and collects the context information according to the aligned core anchor. This plug-and-play module can effectively rectify the misalignment between kernel and objects and extract aligned and robust features. A series of comprehensive experiments are conducted on two large-scale public remote sensing object detection datasets. Experiment results suggest that the proposed method is effective to alleviate the misalignment problem. Compared with the baseline model, the detection accuracy is improved by 8.5% mAP and 11.0% mAP on the challenging benchmark for object detection in optical remote sensing image (DIOR) and a large-scale dataset for object detection in aerial image (DOTA) dataset, respectively. Our best-resulting model achieves the state-of-the-art performance, surpassing other one-stage detectors both on the two datasets at a high detection speed of 21 FPS.

The fusion of infrared and visible images combines the advantages of thermal radiation information in infrared images and texture information in visible images. To preserve salient targets and obtain better fusion results, this paper proposes a novel infrared and visible image fusion method based on dual-kernel side window filtering and detail optimization with S-shaped curve transformation. First, a dual-kernel side window box filter (DSWBF) with adaptive filter kernel size is designed to extract the base and detail layers of the source images. Then, a saliency-based fusion rule is proposed for the base layers to highlight the salient regions of the infrared image. Next, a detail optimization module is constructed based on an S-shaped curve transformation and guided filter to optimize the detail layer of the infrared image. The optimized detail layer is then merged with the detail layer of the visible image to obtain the fused detail layer. Finally, the fusion image is created by reconstructing the fused base and detail layers. Experimental results on the two public datasets indicate that compared with state-of-the-art methods, the proposed method can obtain better fusion performance in terms of subjective and objective evaluations.

Blind pansharpening addresses the problem of generating a high spatial-resolution multi-spectral (HRMS) image given a low spatial-resolution multi-spectral (LRMS) image with the guidance of its associated spatially misaligned high spatial-resolution panchromatic (PAN) image without parametric side information. In this paper, we propose a fast approach to blind pansharpening and achieve state-of-the-art image reconstruction quality. Typical blind pansharpening algorithms are often computationally intensive since the blur kernel and the target HRMS image are often computed using iterative solvers and in an alternating fashion. To achieve fast blind pansharpening, we decouple the solution of the blur kernel and of the HRMS image. First, we estimate the blur kernel by computing the kernel coefficients with minimum total generalized variation that blur a downsampled version of the PAN image to approximate a linear combination of the LRMS image channels. Then, we estimate each channel of the HRMS image using local Laplacian prior to regularize the relationship between each HRMS channel and the PAN image. Solving the HRMS image is accelerated by both parallelizing across the channels and by fast numerical algorithms for each channel. Due to the fast scheme and the powerful priors we used on the blur kernel coefficients (total generalized variation) and on the cross-channel relationship (local Laplacian prior), numerical experiments demonstrate that our algorithm outperforms state-of-the-art model-based counterparts in terms of both computational time and reconstruction quality of the HRMS images., Comment: 17 pages, 47 figures, journal, accepted by IEEE Transactions on Geoscience and Remote Sensing

Integrating compressed sensing (CS) with millimeter-wave (mmW) holography has shown great potential to achieve lightweight onboard hardware, low sampling ratio, and high-speed sensing. However, conventional CS-driven algorithms are always limited by nontrivial adjusting of parameters and excessive computational cost caused by plenty of iterations. To address this problem, we propose a lightweight model-based deep learning framework (LFIST-Net) for mmW 3-D holography, by combining the interpretability of fast iterative shrinkage-thresholding algorithm (FISTA) and tuning-free merit of data-driven deep neural network. First, the single-frequency (SF) holographic imaging technique is integrated into FISTA, which serves as the sensing kernels, to avoid large-scale matrix multiplications. Subsequently, the kernel-based FISTA (KFISTA) is mapped into layer-fixed and parameter-learnable LFIST-Net, whose weights are relaxed to be layer-varied. The updating of key parameters in LFIST-Net, including step sizes, thresholds, and momentum coefficients, are regularized by soft-plus function to ensure the non-negativity and monotonicity. As for 3-D holography implementation, the ``1-D + 2-D'' scheme is adopted, where the matched filtering (MF) and well-trained LFIST-Net are used for range focusing and reconstructions of azimuth slices. Without losing efficiency, the range-focused subechoes are processed parallelly in 3-D cube form. Experiments, including both simulated and measured tests based on a commercial mmW radar, prove that LFIST-Net is capable of reconstructing the imaging scene precisely. In particular, in near-field mmW 3-D holography tests, both numerical and visual results demonstrate LFIST-Net yields compelling reconstruction performance while maintaining high computational speed compared with MF-based, conventional CS-driven, and network-based methods.

In recent days, facial emotion recognition techniques are considered important and critical due wide array of application domains use facial emotion as part of their workflow and analytics gathering. Reduced recognition rate, inefficient computation and increased time consumption are the major drawbacks of these techniques. To overcome these issues, this paper develops the new facial emotion recognition technique named as Minimal Angular Feature Oriented Neural Network based Emotion Prediction (MAFONN-EP). Initially, the input video sequence are categorized into image frames that are pre-processed by eliminating the noise by using Weighted Median Filtering (WMF) technique and to separate the background and foreground regions using Edge Preserved Background Separation and Foreground Extraction (EPBSFE) technique. Then, the set of texture patterns are extracted based on four key parts such as two eyes, nose and mouth by using the Minimal Angular Deviation (MAD) technique. Particular features are selected by employing the Cuckoo Search based Particle Swarm Optimization (CS-PSO) technique that also reduces the feature dimensionality. Finally, the Weight Based Pointing Kernel Classification (WBPKC) technique is employed for recognizing the emotion. In the experimental results, the performance of the proposed technique is analyzed and compared with different performance measures like accuracy, sensitivity, specificity, precision, recall.

We devote our interest in this work to investigate the sufficient conditions for the existence, uniqueness, and Ulam-Hyers stability of solutions for a new fractional system in the frame of Atangana-Baleanu-Caputo fractional operator with multi-parameters Mittag-Leffler kernels investigated lately by Abdeljawad (Chaos: An Interdisciplinary J. Nonlinear Sci. Vol. 29, no. 2, (2019): 023102). Moreover, the continuous dependence of solution and $ \delta $-approximate solutions are analyzed to such a system. Our approach is based on Banach's and Schaefer's fixed point theorems and some mathematical techniques. In order to illustrate the validity of our results, an example is given.

Inevitable corruption and degeneration make the performance of subsequent high-level semantic tasks in hyperspectral images (HSIs) unsatisfactory. Despite that many denoising methods have been proposed, significant room for improvement still remains. To better suppress noise and preserve the HSI spatial–spectral structure, we propose an attention-based Octave dense network. A separable spectral feature extraction module is introduced to extract the spatial–spectral features consistent with the structure prior. The extracted features are fine-tuned by the attention module in both channel and spatial domains; then, several dense denoising blocks are elaborately employed to focus on noise feature learning; in order to focus on high-frequency features, which usually have more noise information, we introduce the Octave kernel to implement these blocks. Experiments based on simulated and real-world noisy images demonstrate that the proposed method outperforms the existing traditional and learning-based methods in both quantitative evaluations and visual effects, benefiting the subsequent classification task. In addition, the effectiveness of each module is proven by ablation experiments. Our source code is made available at: https://github.com/LbzSteven/AODN.

Hyperspectral images (HSIs) have gained high spectral resolution due to recent advances in spectral imaging technologies. This incurs problems, such as an increased data scale and an increased number of bands for HSIs, which results in a complex correlation between different bands. In the applications of remote sensing and earth observation, ground objects represented by each HSI pixel are composed of physical and chemical non-Euclidean structures, and HSI classification (HIC) is becoming a more challenging task. To solve the above problems, we propose a framework based on a deep attention graph convolutional network (DAGCN). Specifically, we first integrate an attention mechanism into the spectral similarity measurement to aggregate similar spectra. Therefore, we propose a new similarity measurement method, i.e., the mixed measurement of a kernel spectral angle mapper and spectral information divergence (KSAM-SID), to aggregate similar spectra. Considering the non-Euclidean structural characteristics of HSIs, we design deep graph convolutional networks (DeepGCNs) as a feature extraction method to extract deep abstract features and explore the internal relationship between HSI data. Finally, we dynamically update the attention graph adjacency matrix to adapt to the changes in each feature graph. Experiments on three standard HSI data sets, namely, the Indian Pines, Pavia University, and Salinas data sets, demonstrate that the DAGCN outperforms the baselines in terms of various evaluation criteria. For example, on the Indian Pines data set, the overall accuracy of the proposed method achieves 98.61% when the training sample is 10%.

Recently, feature pyramid has been widely exploited in remote sensing detectors, which greatly alleviates the problem arising from scale variation across objects in remote sensing images. However, these object detectors with feature pyramid give insufficient consideration that objects in remote sensing images usually maintain symmetrical shape. To address this issue, we propose an anchor-free-based detector called Centerness-Aware Network (CANet), which could capture the symmetrical shape of objects in remote sensing images. The kernel structure of CANet is a new Centerness-Aware Model (CAM) that contains three components: Multiscale Centerness Descriptor (MSCD), Centerness Detection Head (CDH), and Feature Selective Module (FSM). Considering that symmetrical objects will maintain a rigid appearance around their center region, three components are integrated into the feature pyramid to extract and utilize the features around the center region. More precisely, the MSCD is embedded into the feature pyramid and highlights the center of current objects through the attention mechanism. Guided by the MSCD, the CDH could accurately capture the center of objects by per-pixel prediction. Furthermore, the FSM is connected to the CDH, which guides the CDH to adaptively select the optimal feature level from the pyramidal features. The selected feature level could describe the best semantic information around the center region, which helps the network progressively fit the symmetrical shape of remote sensing objects. Besides, we also design the hybrid loss function to effectively train CAM in the end-to-end way. The experiments show that our network is competitive with some state-of-the-art detection networks.

The depthwise separable convolution is commonly seen in convolutional neural networks (CNNs), and is widely used to reduce the computation overhead of a standard multi-channel 2D convolution. Existing implementations of depthwise separable convolutions target accelerating model training with large batch sizes with a large number of samples to be processed at once. Such approaches are inadequate for small-batch-sized model training and the typical scenario of model inference where the model takes in a few samples at once. This article aims to bridge the gap of optimizing depthwise separable convolutions by targeting the GPU architecture. We achieve this by designing two novel algorithms to improve the column and row reuse of the convolution operation to reduce the number of memory operations performed on the width and the height dimensions of the 2D convolution. Our approach employs a dynamic tile size scheme to adaptively distribute the computational data across GPU threads to improve GPU utilization and to hide the memory access latency. We apply our approach on two GPU platforms: an NVIDIA RTX 2080Ti GPU and an embedded NVIDIA Jetson AGX Xavier GPU, and two data types: 32-bit floating point (FP32) and 8-bit integer (INT8). We compared our approach against cuDNN that is heavily tuned for the NVIDIA GPU architecture. Experimental results show that our approach delivers over $2\times$ 2 × (up to $3\times$ 3 × ) performance improvement over cuDNN. We show that, when using a moderate batch size, our approach averagely reduces the end-to-end training time of MobileNet and EfficientNet by 9.7 and 7.3 percent respectively, and reduces the end-to-end inference time of MobileNet and EfficientNet by 12.2 and 11.6 percent respectively.

Effectively recognizing different sceneries with complex backgrounds and varied lighting conditions plays an important role in modern AI systems. Competitive performance has recently been achieved by the deep scene categorization models. However, these models implicitly hypothesize that the image-level labels are 100% correct, which is too restrictive. Practically, the image-level labels for massive-scale scenery sets are usually calculated by external predictors such as ImageNet-CN. These labels can easily become contaminated because no predictors are completely accurate. This article proposes a new deep architecture that calculates scene categories by hierarchically deriving stable templates, which are discovered using a generative model. Specifically, we first construct a semantic space by incorporating image-level labels using subspace embedding. Afterward, it is noticeable that in the semantic space, the superpixel distributions from identically labeled images remain unchanged, regardless of the image-level label noises. On the basis of this observation, a probabilistic generative model learns the stable templates for each scene category. To deeply represent each scenery category, a novel aggregation network is developed to statistically concatenate the CNN features learned from scene annotations predicted by HSA. Finally, the learned deep representations are integrated into an image kernel, which is subsequently incorporated into a multiclass SVM for distinguishing scene categories. Thorough experiments have shown the performance of our method. As a byproduct, an empirical study of 33 SIFT-flow categories shows that the learned stable templates remain almost unchanged under a nearly 36% image label contamination rate.

The block-matching and 3D filtering strategy is a recent adequate solution for different problems related to low-pass image filter; however, this technique presents some drawbacks. In fact, handling noise with variable density and average or Gaussian noise with high densities may limit the performance of this approach. To overcome these limitations and increase the performance of the original BM3D approach, we propose in this work a modified BM3D technique based on wave atom transform with adaptive threshold called BM3D-AT. Experimental results and simulations revealed that the proposed technique is more efficient in removing different types of noise from different images, hence achieving outstanding performances.

We propose numerical schemes for solving the boundary value problem for the modified Helmholtz equation and generalized impedance boundary condition. The approaches are based on the reduction of the problem to the boundary integral equation with a hyper-singular kernel. In the first scheme the hyper-singular integral operator is treated by splitting off the singularity technique whereas in the second scheme the idea of numerical differentiation is employed. The solvability of the boundary integral equation and convergence of the first method are established. Exponential convergence for analytic data is exhibited by numerical examples. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.Y. All rights reserved.

Total Lagrangian - Smoothed Particle Hydrodynamics (TL-SPH) is a classical algorithm to improve the tensile instability encountered in the conventional Eulerian kernel-based SPH method for solid mechanics. Meanwhile, the gradient correction usually is employed in TL-SPH to enhance the consistency of approximation, which would generate unavoidable time costs. Therefore, a simplified gradient correction under the initial configuration is derived. Aiming at different particle configurations, the corresponding selection modes are provided and verified. Then the simplified gradient correction is introduced into TL-SPH, which is the Improved TL-SPH (ITL-SPH) method. Firstly, a classical example – elastic rubber collision is simulated to verify ITL-SPH has the same ability as TL-SPH to improve tensile instability. Then, the convergence and high efficiency of ITL-SPH is demonstrated via the uniaxial tension of a plate. Moreover, the plate with square and triangle holes also is simulated to show the adaptation and stability of ITL-SPH for the extreme boundary. Under the Total Lagrangian frame, the treatment to model the crack is necessary. So, this paper proposes a damage model and combines it with the ITL-SPH method for crack initiation and propagation, which is verified by the plate with holes and the notched beam examples.

Perturbations are ubiquitous in metabolism. A central tool to understand and control their influence on metabolic networks is sensitivity analysis, which investigates how the network responds to external perturbations. We follow here a structural approach: the analysis is based on the network stoichiometry only and it does not require any quantitative knowledge of the reaction rates. We consider perturbations of reaction rates and metabolite concentrations, at equilibrium, and we investigate the responses in the network. For general metabolic systems, this paper focuses on the sign of the responses, i.e. whether a response is positive, negative or whether its sign depends on the parameters of the system. In particular, we identify and describe the subnetworks that are the main players in the sign description. These subnetworks are associated to certain kernel vectors of the stoichiometric matrix and are thus independent from the chosen kinetics., Comment: 29 pages, 5 figures

In this paper, an in-depth study and analysis of attribute modelling and knowledge acquisition of massive images are conducted using image recognition. For the complexity of association relationships between attributes of incomplete data, a single-output subnetwork modelling method for incomplete data is proposed to build a neural network model with each missing attribute as output alone and other attributes as input in turn, and the network structure can deeply portray the association relationships between each attribute and other attributes. To address the problem of incomplete model inputs due to the presence of missing values, we propose to treat and describe the missing values as system-level variables and realize the alternate update of network parameters and dynamic filling of missing values through iterative learning among subnets. The method can effectively utilize the information of all the present attribute values in incomplete data, and the obtained subnetwork population model is a fit to the attribute association relationships implied by all the present attribute values in incomplete data. The strengths and weaknesses of existing image semantic modelling algorithms are analysed. To reduce the workload of manually labelling data, this paper proposes the use of a streaming learning algorithm to automatically pass image-level semantic labels to pixel regions of an image, where the algorithm does not need to rely on external detectors and a priori knowledge of the dataset. Then, an efficient deep neural network mapping algorithm is designed and implemented for the microprocessing architecture and software programming framework of this edge processor, and a layout scheme is proposed to place the input feature maps outside the kernel DDR and the reordered convolutional kernel matrices inside the kernel storage body and to design corresponding efficient vectorization algorithms for the multidimensional matrix convolution computation, multidimensional pooling computation, local linear normalization, etc., which exist in the deep convolutional neural network model. The efficient vectorized mapping scheme is designed for the multidimensional matrix convolution computation, multidimensional pooling computation, local linear normalization, etc. in the deep convolutional neural network model so that the utilization of MAC components in the core loop can reach 100%.

Advances in image object detection lead to applying deep convolution neural networks in the document image analysis domain. Unlike general colorful and pattern-rich objects, tables in document images have properties that limit the capacity of deep learning structures. Significant variation in size and aspect ratio and the local similarity among document components are the main challenges that require both global features for detection and local features for the separation of nearby objects. To deal with these challenges, we present TableSegNet, a compact architecture of a fully convolutional network to detect and separate tables simultaneously. TableSegNet consists of a deep convolution path to detect table regions in low resolution and a shallower path to locate table locations in high resolution and split the detected regions into individual tables. To improve the detection and separation capacity, TableSegNet uses convolution blocks of wide kernel sizes in the feature extraction process and an additional table-border class in the main output. With only 8.1 million parameters and trained purely on document images from the beginning, TableSegNet has achieved state-of-the-art F1 score at the IoU threshold of 0.9 on the ICDAR2019 and the highest number of correctly detected tables on the ICDAR2013 table detection datasets.

An $H$-free editing problem asks whether we can edit at most $k$ edges to make a graph contain no induced copy of the fixed graph $H$. We obtain a polynomial kernel for this problem when $H$ is a diamond. The incompressibility dichotomy for $H$ being a 3-connected graph and the classical complexity dichotomy suggest that except for $H$ being a complete/empty graph, $H$-free editing problems admit polynomial kernels only for a few small graphs $H$. Therefore, we believe that our result is an essential step toward a complete dichotomy on the compressibility of $H$-free editing. Additionally, we give a cubic-vertex kernel for the diamond-free edge deletion problem, which is far simpler than the previous kernel of the same size for the problem.

Subpixel mapping (SPM) is an effective way to solve the mixed pixel problem, which is a ubiquitous phenomenon in remotely sensed imagery, by characterizing subpixel distribution within the mixed pixels. In fact, the majority of the classical and state-of-the-art SPM algorithms can be viewed as a convolution process, but these methods rely heavily on fixed and handcrafted kernels that are insufficient in characterizing a geographically realistic distribution image. In addition, the traditional SPM approach is based on the prerequisite of abundance images derived from spectral unmixing (SU), during which process uncertainty inherently exists and is propagated to the SPM. In this article, a kernel-learnable convolutional neural network (CNN) framework for subpixel mapping (SPMCNN-F) is proposed. In SPMCNN-F, the kernel is learnable during the training stage based on the given training sample pairs of low- and high-resolution patches for learning a geographically realistic prior, instead of fixed priors. The end-to-end mapping structure enables direct subpixel information extraction from the original coarse image, avoiding the uncertainty propagation from the SU. In the experiments undertaken in this study, two state-of-the-art super-resolution networks were selected as application demonstrations of the proposed SPMCNN-F method. In experiment part, three hyperspectral image data sets were adopted, two in a synthetic coarse image approach and one in a real coarse image approach, for the validation. Additionally, a new data set with pairs of Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat images were adopted in a real coarse image approach, for further validation of SPMCNN-F in large-scale area. The restored fine distribution images obtained in all the experiments showed a perceptually better reconstruction quality, both qualitatively and quantitatively, confirming the superiority of the proposed SPM framework.

For diagonal sparse matrices that have many long zero sections or scatter points or diagonal deviations from the main diagonal, a great number of zeros need be filled to maintain the diagonal structure while using DIA to store them, which leads to the performance degradation of the existing DIA kernels because the padded zeros consume extra computation and memory resources. This motivates us to present an adaptive sparse matrix-vector multiplication (SpMV) for diagonal sparse matrices on the graphics processing unit (GPU), called DIA-Adaptive, to alleviate the drawback of DIA kernels for these cases. For DIA-Adaptive, there are the following characteristics: (1) two new sparse storage formats, BRCSD (Diagonal Compressed Storage based on Row-Blocks)-I and BRCSD-II, are proposed to adapt it to various types of diagonal sparse matrices besides adopting DIA, and SpMV kernels corresponding to these storage formats are presented; and (2) a search engine is designed to choose the most appropriate storage format from DIA, BRCSD-I, and BRCSD-II for any given diagonal sparse matrix; and (3) a code generator is presented to automatically generate SpMV kernels. Using DIA-Adaptive, the ideal storage format and kernel are automatically chosen for any given diagonal sparse matrix, and thus high performance is achieved. Experimental results show that our proposed DIA-Adaptive is effective, and has high performance and good parallelism, and outperforms the state-of-the-art SpMV algorithms for all test cases.

The present paper is devoted to the problem of stabilization of the one-dimensional semilinear heat equation with nonlocal initial conditions. The control is with boundary actuation. It is linear, of finite-dimensional structure, given in an explicit form. It allows to write the corresponding solution of the closed-loop equation in a mild formulation via a kernel, then to apply a fixed point argument in a convenient space.

Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among various topics, sparse-view CT remains a challenge which targets a decent image reconstruction from ultra-sparse projections. To address this challenge, in this article we propose a Dual-domain Residual-based Optimization NEtwork (DRONE). DRONE consists of three modules respectively for embedding, refinement, and awareness. In the embedding module, a sparse sinogram is first extended. Then, sparse-view artifacts are effectively suppressed by the image domain networks. After that, the refinement module focuses on the recovery of image details in the residual data and image domains synergistically. Finally, the results from embedding and refinement components in the data and image domains are regularized for optimized image quality in the awareness module, which ensures the consistency between measurements and images with the kernel awareness of compressed sensing. The DRONE network is trained, validated, and tested on preclinical and clinical datasets, demonstrating its merits in edge preservation, feature recovery, and reconstruction accuracy.

The multi-exposure fusion (MEF) methods have received much attention in recent years due to the importance of constructing high dynamic range images. Among most of the existing studies, multi-scale structural-patch-decomposition-based MEF (MSPD-MEF) has achieved state-of-the-art fusion quality and the fastest running time. However, this method still suffers from detail loss in the fused images. To tackle this issue, we first incorporate the edge-preserving factors into this method to preserve the details in the fused images in a single-scale setting. Then, we develop the novel and flexible bell curve function, which can further preserve the details in both bright and dark regions. After that, we also show that our method can seamlessly plug in to this multi-scale framework. Extensive experimental results indicate that the proposed method can produce pleasing fusion results with little artifacts and low computational cost in both static and dynamic scenes.

Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, the standard convolution traverses the input images/features using a sliding window scheme to extract features. However, not all the windows contribute equally to the prediction results of CNNs. In practice, the convolutional operation on some of the windows (e.g., smooth windows that contain very similar pixels) can be very redundant and may introduce noises into the computation. Such redundancy may not only deteriorate the performance but also incur the unnecessary computational cost. Thus, it is important to reduce the computational redundancy of convolution to improve the performance. To this end, we propose a Content-aware Convolution (CAC) that automatically detects the smooth windows and applies a 1 ×1 convolutional kernel to replace the original large kernel. In this sense, we are able to effectively avoid the redundant computation on similar pixels. By replacing the standard convolution in CNNs with our CAC, the resultant models yield significantly better performance and lower computational cost than the baseline models with the standard convolution. More critically, we are able to dynamically allocate suitable computation resources according to the data smoothness of different images, making it possible for content-aware computation. Extensive experiments on various computer vision tasks demonstrate the superiority of our method over existing methods.