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Recently, super-resolution (SR) has achieved superior performance with the development of deep learning. However, previous methods usually require considerable computational resources with a large model size, which hinders practical applications. To achieve real-time inference and high quality for SR, this paper presents an edge-priority-extraction network, which is constructed with our proposed edge-priority blocks (EPB). The EPB utilizes multiple branches with edge information to further improve the network representation. Moreover, it can be re-parameterized for efficient inference. For more effective utilization of edge information, this paper also proposes the mix-priority filter with edge extraction of horizontal and vertical priorities to improve the network performance. The filters can adaptively extract the edge information with multi-direction derivatives. The experimental results show that our models can use less computational cost to meet the real-time demand and have a better SR performance than the recent real-time SR models. [ABSTRACT FROM AUTHOR]

Due to the considerable computational demands of physics-based numerical weather prediction, especially when modeling fine-grained spatio-temporal atmospheric phenomena, deep learning methods offer an advantageous approach by leveraging specialized computing devices to accelerate training and significantly reduce computational costs. Consequently, the application of deep learning methods has presented a novel solution in the field of weather forecasting. In this context, we introduce a groundbreaking deep learning-based weather prediction architecture known as Hierarchical U-Net (HU-Net) with re-parameterization techniques. The HU-Net comprises two essential components: a feature extraction module and a U-Net module with re-parameterization techniques. The feature extraction module consists of two branches. First, the global pattern extraction employs adaptive Fourier neural operators and self-attention, well-known for capturing long-term dependencies in the data. Second, the local pattern extraction utilizes convolution operations as fundamental building blocks, highly proficient in modeling local correlations. Moreover, a feature fusion block dynamically combines dual-scale information. The U-Net module adopts RepBlock with re-parameterization techniques as the fundamental building block, enabling efficient and rapid inference. In extensive experiments carried out on the large-scale weather benchmark dataset WeatherBench at a resolution of 1.40625 ∘ , the results demonstrate that our proposed HU-Net outperforms other baseline models in both prediction accuracy and inference time. [ABSTRACT FROM AUTHOR]

Aiming at the two problems that most existing deep network-based fusion approaches have complex network architecture with high computation cost, and they fail to adequately consider the intrinsic characteristics of multi-modal images which results in insufficient information interaction of cross-modality features, a re-parameterized infrared and visible image fusion network based on coordinate attention is proposed. In this network, re-parameterization technique is introduced and combined with residual learning to perform feature extraction for computing efficiency and satisfying fusion quality. Moreover, to improve the interactivity of cross-modality features and fully utilize multi-modal image information, a coordinate attention-based fusion module is devised to yield fused feature. Considering the information loss during extracting process, a fused feature enhance module which leverages the preceding cross-modality features to implement feature compensation is further developed. Extensive experiments demonstrate that the proposed method not only has lower computational cost, but also achieves the improvement of multiple objective evaluation metrics while ensuring good visual effects. [ABSTRACT FROM AUTHOR]

Forest is the largest carbon reservoir and carbon absorber on earth. Thus, mapping forest cover change accurately is of great significance to achieving the global carbon neutrality goal. Accurate forest change information could be acquired by deep learning methods using high-resolution remote sensing images. However, deforestation detection based on deep learning on a large-scale region with high-resolution images required huge computational resources. Therefore, there was an urgent need for a fast and accurate deforestation detection model. In this study, we proposed an interesting but effective re-parameterization deforestation detection model, named RepDDNet. Unlike other existing models designed for deforestation detection, the main feature of RepDDNet was its decoupling feature, which means that it allowed the multi-branch structure in the training stages to be converted into a plain structure in the inference stage, thus the computation efficiency can be significantly improved in the inference stage while maintaining the accuracy unchanged. A large-scale experiment was carried out in Ankang city with 2-meter high-resolution remote sensing images (the total area of it was over 20,000 square kilometers), and the result indicated that the model computation efficiency could be improved by nearly 30% compared with the model without re-parameterization. Additionally, compared with other lightweight models, RepDDNet also displayed a trade-off between accuracy and computation efficiency.

Apple leaf diseases without timely control will affect fruit quality and yield, intelligent detection of apple leaf diseases was especially important. So this paper mainly focuses on apple leaf disease detection problem, proposes a machine vision algorithm model for fast apple leaf disease detection called LALNet (High-speed apple leaf network). First, an efficient sacked module for apple leaf detection, known as EALD (efficient apple leaf detection stacking module), was designed by utilizing the multi-branch structure and depthseparable modules. In the backbone network of LALNet, (High-speed apple leaf network) four layers of EALD modules were superimposed and an SE (Squeeze-and-Excitation) module was added in the last layer of the model to improve the attention of the model to important features. A structural reparameterization technique was used to combine the outputs of two layers of deeply separable convolutions in branch during the inference phase to improve the model’s operational speed. The results show that in the test set, the detection accuracy of the model was 96.07%. The total precision was 95.79%, the total recall was 96.05%, the total F1 was 96.06%, the model size was 6.61 MB, and the detection speed of a single image was 6.68 ms. Therefore, the model ensures both high detection accuracy and fast execution speed, making it suitable for deployment on embedded devices. It supports precision spraying for the prevention and control of apple leaf disease. [ABSTRACT FROM AUTHOR]

Simple Summary: Achieving automatic detection of plant diseases in real agricultural scenarios where low-computing-power platforms are deployed is a significant research topic. As fine-grained agriculture continues to expand and farming methods deepen, traditional manual detection methods demand a high labor intensity. In recent years, the rapid advancement of computer network vision has greatly enhanced the computer-processing capabilities for pattern recognition problems across various industries. Consequently, a deep neural network based on an automatic pruning mechanism is proposed to enable high-accuracy plant disease detection even under limited computational power. Furthermore, an application is developed based on this method to expedite the translation of theoretical results into practical application scenarios. Timely and accurate detection of plant diseases is a crucial research topic. A dynamic-pruning-based method for automatic detection of plant diseases in low-computing situations is proposed. The main contributions of this research work include the following: (1) the collection of datasets for four crops with a total of 12 diseases over a three-year history; (2) the proposition of a re-parameterization method to improve the boosting accuracy of convolutional neural networks; (3) the introduction of a dynamic pruning gate to dynamically control the network structure, enabling operation on hardware platforms with widely varying computational power; (4) the implementation of the theoretical model based on this paper and the development of the associated application. Experimental results demonstrate that the model can run on various computing platforms, including high-performance GPU platforms and low-power mobile terminal platforms, with an inference speed of 58 FPS, outperforming other mainstream models. In terms of model accuracy, subclasses with a low detection accuracy are enhanced through data augmentation and validated by ablation experiments. The model ultimately achieves an accuracy of 0.94. [ABSTRACT FROM AUTHOR]

With the supervised deep learning algorithms, it is prone to generate artifacts when restoring the blurred solar speckle images taken by Yunnan Observatories, and it has a long training time and over-reliance on reference images, this paper proposed a weakly supervised method based on structural reparameterization combined with multi-branch module to reconstruct solar speckle images. First, deblurring model combined single-scale and multi-scale network to design, with constructing multi-branch modules to extract features of different scales, enhance detailed information, and reduce the generation of artifacts; second, each branch structure re-parameterized to make the reuse of structure parameters runs through the entire feature extraction process; after that, the deblurring model embedded in the weakly supervised training, the blurred image assorted firstly, then the degradation model used to learn different levels of degradation. Constituted paired dataset of corresponding levels, and the deblurring model used to inversely degenerate the dataset to reconstruct solar speckle images. Experimental results show that compared with the existing deblurring method, the proposed method has higher model training efficiency and less dependence on reference images, which can meet the high-resolution reconstruction requirements of solar speckle images. [ABSTRACT FROM AUTHOR]

Explainability in medical images analysis plays an important role in the accurate diagnosis and treatment of tumors, which can help medical professionals better understand the images analysis results based on deep models. This paper proposes an explainable brain tumor detection framework that can complete the tasks of segmentation, classification, and explainability. The re-parameterization method is applied to our classification network, and the effect of explainable heatmaps is improved by modifying the network architecture. Our classification model also has the advantage of post-hoc explainability. We used the BraTS-2018 dataset for training and verification. Experimental results show that our simplified framework has excellent performance and high calculation speed. The comparison of results by segmentation and explainable neural networks helps researchers better understand the process of the black box method, increase the trust of the deep model output, and make more accurate judgments in disease identification and diagnosis. [ABSTRACT FROM AUTHOR]

Forest is the largest carbon reservoir and carbon absorber on earth. Thus, mapping forest cover change accurately is of great significance to achieving the global carbon neutrality goal. Accurate forest change information could be acquired by deep learning methods using high-resolution remote sensing images. However, deforestation detection based on deep learning on a large-scale region with high-resolution images required huge computational resources. Therefore, there was an urgent need for a fast and accurate deforestation detection model. In this study, we proposed an interesting but effective re-parameterization deforestation detection model, named RepDDNet. Unlike other existing models designed for deforestation detection, the main feature of RepDDNet was its decoupling feature, which means that it allowed the multi-branch structure in the training stages to be converted into a plain structure in the inference stage, thus the computation efficiency can be significantly improved in the inference stage while maintaining the accuracy unchanged. A large-scale experiment was carried out in Ankang city with 2-meter high-resolution remote sensing images (the total area of it was over 20,000 square kilometers), and the result indicated that the model computation efficiency could be improved by nearly 30% compared with the model without re-parameterization. Additionally, compared with other lightweight models, RepDDNet also displayed a trade-off between accuracy and computation efficiency. [ABSTRACT FROM AUTHOR]

Gastric cancer is the third most common cause of cancer-related death in the world. Human epidermal growth factor receptor 2 (HER2) positive is an important subtype of gastric cancer, which can provide significant diagnostic information for gastric cancer pathologists. However, pathologists usually use a semi-quantitative assessment method to assign HER2 scores for gastric cancer by repeatedly comparing hematoxylin and eosin (H&E) whole slide images (WSIs) with their HER2 immunohistochemical WSIs one by one under the microscope. It is a repetitive, tedious, and highly subjective process. Additionally, WSIs have billions of pixels in an image, which poses computational challenges to Computer-Aided Diagnosis (CAD) systems. This study proposed a deep learning algorithm for HER2 quantification evaluation of gastric cancer. Different from other studies that use convolutional neural networks for extracting feature maps or pre-processing on WSIs, we proposed a novel automatic HER2 scoring framework in this study. In order to accelerate the computational process, we proposed to use the re-parameterization scheme to separate the training model from the deployment model, which significantly speedup the inference process. To the best of our knowledge, this is the first study to provide a deep learning quantification algorithm for HER2 scoring of gastric cancer to assist the pathologist's diagnosis. Experiment results have demonstrated the effectiveness of our proposed method with an accuracy of 0.94 for the HER2 scoring prediction. [ABSTRACT FROM AUTHOR]

Deep saliency models can effectively imitate the attention mechanism of human vision, and they perform considerably better than classical models that rely on handcrafted features. However, deep models also require higher-level information, such as context or emotional content, to further approach human performance. Therefore, this study proposes a multilevel saliency prediction network that aims to use a combination of spatial and channel information to find possible high-level features, further improving the performance of a saliency model. Firstly, we use a VGG style network with an identity block as the primary network architecture. With the help of re-parameterization, we can obtain rich features similar to multiscale networks and effectively reduce computational cost. Secondly, a subnetwork with a channel attention mechanism is designed to find potential saliency regions and possible high-level semantic information in an image. Finally, image spatial features and a channel enhancement vector are combined after quantization to improve the overall performance of the model. Compared with classical models and other deep models, our model exhibits superior overall performance. [ABSTRACT FROM AUTHOR]

Gastric cancer is the third most common cause of cancer-related death in the world. Human epidermal growth factor receptor 2 (HER2) positive is an important subtype of gastric cancer, which can provide significant diagnostic information for gastric cancer pathologists. However, pathologists usually use a semi-quantitative assessment method to assign HER2 scores for gastric cancer by repeatedly comparing hematoxylin and eosin (H&E) whole slide images (WSIs) with their HER2 immunohistochemical WSIs one by one under the microscope. It is a repetitive, tedious, and highly subjective process. Additionally, WSIs have billions of pixels in an image, which poses computational challenges to Computer-Aided Diagnosis (CAD) systems. This study proposed a deep learning algorithm for HER2 quantification evaluation of gastric cancer. Different from other studies that use convolutional neural networks for extracting feature maps or pre-processing on WSIs, we proposed a novel automatic HER2 scoring framework in this study. In order to accelerate the computational process, we proposed to use the re-parameterization scheme to separate the training model from the deployment model, which significantly speedup the inference process. To the best of our knowledge, this is the first study to provide a deep learning quantification algorithm for HER2 scoring of gastric cancer to assist the pathologist's diagnosis. Experiment results have demonstrated the effectiveness of our proposed method with an accuracy of 0.94 for the HER2 scoring prediction.

Timely and accurate detection of plant diseases is a crucial research topic. A dynamic-pruning-based method for automatic detection of plant diseases in low-computing situations is proposed. The main contributions of this research work include the following: (1) the collection of datasets for four crops with a total of 12 diseases over a three-year history; (2) the proposition of a re-parameterization method to improve the boosting accuracy of convolutional neural networks; (3) the introduction of a dynamic pruning gate to dynamically control the network structure, enabling operation on hardware platforms with widely varying computational power; (4) the implementation of the theoretical model based on this paper and the development of the associated application. Experimental results demonstrate that the model can run on various computing platforms, including high-performance GPU platforms and low-power mobile terminal platforms, with an inference speed of 58 FPS, outperforming other mainstream models. In terms of model accuracy, subclasses with a low detection accuracy are enhanced through data augmentation and validated by ablation experiments. The model ultimately achieves an accuracy of 0.94.

Explainability in medical images analysis plays an important role in the accurate diagnosis and treatment of tumors, which can help medical professionals better understand the images analysis results based on deep models. This paper proposes an explainable brain tumor detection framework that can complete the tasks of segmentation, classification, and explainability. The re-parameterization method is applied to our classification network, and the effect of explainable heatmaps is improved by modifying the network architecture. Our classification model also has the advantage of post-hoc explainability. We used the BraTS-2018 dataset for training and verification. Experimental results show that our simplified framework has excellent performance and high calculation speed. The comparison of results by segmentation and explainable neural networks helps researchers better understand the process of the black box method, increase the trust of the deep model output, and make more accurate judgments in disease identification and diagnosis.

Recent advancements in deep learning have notably advanced the field of image denoising. Yet, blindly increasing the depth or width of convolutional neural networks (CNNs) cannot ameliorate the network effectively, and even leads to training difficulties and sophisticated training tricks. In this paper, a lightweight CNN with heterogeneous kernels (HKCNN) is designed for efficient noise removal. HKCNN comprises four modules: a multiscale block (MB), an attention enhancement block (AEB), an elimination block (EB), and a construct block (CB). Specifically, the MB leverages heterogeneous kernels alongside re-parameterization to capture diverse complementary structure information, bolstering discriminative ability and the denoising robustness of the denoiser. The AEB incorporates an attention mechanism that prioritizes salient features, expediting the training stage and boosting denoising efficacy. The EB and CB are designed to further suppress noise and reconstruct latent clean images. Besides, the HKCNN integrates perceptual loss for both retaining semantic details and improving image perceptual quality, so as to refine the denoising output. Comprehensive qualitative and quantitative evaluations highlight the superior performance of HKCNN over state-of-the-art denoising methods, validating its efficacy in practical image denoising scenarios. [ABSTRACT FROM AUTHOR]