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Multimodal image fusion is the process of combing relevant biological information that can be used for automated industrial application. In this article, we present a novel framework combining fractal constraint with group sparsity to achieve the optimal fusion quality. First, we adopt the idea of patch division and componentwise separation to perceive the fractal characteristics across multimodality sources. Then, to preserve the spatial information against the redundancy of component-entanglement, the group sparsity is proposed. A dual variable weighting rule is inherently embedded to mitigate the overfitting across the component penalty. Furthermore, the alternating direction method of multipliers is conducted to the proposed model optimization. The experiments show that our model has a better performance in quantitative visual quality and qualitative evaluation analysis. Finally, a real segmentation application of positron emission tomography/computed tomography image fusion proves the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Detecting the small targets from a heterogeneous background in an infrared image is a challenging problem, which has received extensive attention. In this article, we propose a method in terms of tensor ring (TR) decomposition and nonlinear multiscale morphological top-hat transformation for infrared small target detection (ISTD). First, a tensor model with prior knowledge is constructed for extracting the structural features of multiple infrared images. Then, the problem of small target detection is converted into a problem of minimizing the tensor rank with TR. Based on the TR decomposition model, we introduce the top-hat regularization into our model with multiple structural elements of different size to perform morphological operations. The corresponding morphological model exploits a more accurate ring top-hat regularization expression through adaptive nonlinear combination for the ISTD problem. Finally, the optimization of the model is realized by the closed solution given by the alternating direction method of multipliers algorithm. In order to verify the superior performance of our method, our method is compared with a number of advanced detection models. By analyzing the results of comparison experiments, the detection accuracy and precision of our model in the detection of small infrared targets have been improved. Even in complex background conditions, our model also maintain a good robustness. [ABSTRACT FROM AUTHOR]

Infrared small target detection is a research hotspot in computer vision technology that plays an important role in infrared early warning systems. Specifically, infrared images with strong background clutter and noise pose a challenge to target detection technology. In this article, we propose a method for infrared small target detection based on the double nuclear norm and ring-structural elements over a generalized tensor framework. We use the double nuclear norm instead of the traditional single nuclear norm as the relaxation of the rank function, which solves the problem that the suboptimal solution deviates from the original solution and better approaches the rank minimization. In addition, we use weighted ring structural elements instead of traditional structural elements to make better use of the target information and its surrounding background. Experiments on six sequences of real images show that the proposed method can enhance the target and suppress the background effectively, and ensure a high detection probability and a low false alarm rate. [ABSTRACT FROM AUTHOR]

With the development of spectral detection and photoelectric imaging, multiband spectrum is always degraded by the random noise and band overlap during the acquisition of spectrum devices. Owing to the fixed spectrum degradation model, the existing spectrum deconvolution technologies are sensitive to the handcrafted model designed and manually selected parameters. The fundamental cause of these limitations during spectral analysis is that spectral processing is limited by 1-D signal without structural information available and insufficient training samples. In this article, a dual stream neural network is proposed to reconstruct the original infrared spectroscopy, which effectively strengthens the capability to represent the feature of infrared spectrum. A novel activation function is proposed to realize the function of the dual stream network. Furthermore, a heuristic learning strategy from the perspective of balanced self-paced learning is exploited to help network train from simple to difficult, resolving the problem of high sample repeatability. Compared with other traditional methods, the experimental results show that our network can achieve state-of-the-art reconstruction result and fairly excellent performance in terms of the corresponding index within synectics and real spectrum experiments. [ABSTRACT FROM AUTHOR]

Infrared small target detection is a key technique in an infrared system. In the past decade, many methods have concentrated on traditional top-hat transformation, which relies on the hand-crafted shape and value of structural elements. However, these methods are inevitably challenged by the following two aspects: first, the structural elements cannot suppress heavy clutter because the construction of structural elements is always according to the prior information of the target and unable to consider the feature of clutter. Second, adaptively extracting sufficient local feature information for background suppression is hard for the structural element. In this article, we propose an entropy-driven top-hat transformation with guided filter kernel for considering the features of both the clutters and background. First, we propose an entropy-driven top-hat transformation method with our proposed local mean entropy, which can be used to suppress clutter according to the local complex degree of clutter. Then, an adaptive structural element based on a guided filter kernel is further exploited to capture the local feature information of image for background suppression. Finally, an adaptive threshold is combined with our algorithm to achieve target detection in image sequences. The experimental results show that the proposed algorithm is not only robust for suppressing different kinds of backgrounds but can also obtain a higher value of the signal-to-clutter ratio gain and detection accuracy compared with some popular traditional baseline methods and related top-hat methods. [ABSTRACT FROM AUTHOR]

Image segmentation is a key component of image analysis, which refers to the process of partitioning the image into multiple segments. Graph cut is widely used in image segmentation by constructing a graph that the minimal cut of this graph would lead to partition the corresponding pixels of the different objects. In this paper, we reconstruct the graph cut problem as a special non-convex optimization problem instead of the traditional maximum flow problem. We extend this non-convex problem to the hypergraph method and combine it with a tensor field based on a directional bilateral filter bank to achieve segmentation in grayscale images. Accordingly, an efficient minimization algorithm is proposed to solve this non-convex problem with global convergence. Furthermore, we have selected the data of BSDS300 and BSDS500 as tests. Experimental results and evaluation index tests further demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Establishing correspondence between two given geometrical graph structures is an important problem in computer vision and pattern recognition. In this paper, we propose a robust graph matching (RGM) model to improve the effectiveness and robustness on the matching graphs with deformations, rotations, outliers, and noise. First, we embed the joint geometric transformation into the graph matching model, which performs unary matching over graph nodes and local structure matching over graph edges simultaneously. Then, the $L_{2,1}$ -norm is used as the similarity metric in the presented RGM to enhance the robustness. Finally, we derive an objective function which can be solved by an effective optimization algorithm, and theoretically prove the convergence of the proposed algorithm. Extensive experiments on various graph matching tasks, such as outliers, rotations, and deformations show that the proposed RGM model achieves competitive performance compared to the existing methods. [ABSTRACT FROM AUTHOR]

In the fields of industry research, infrared spectrometers are widely used in diverse applications. However, the spectrum often suffers from band overlap and random noise due to the distortion caused by the point spread function, especially for aging instruments. The problem of reconstructing the clear spectrum from the degraded spectrum is called spectrum deconvolution. Traditional partial differential equation (PDE) methods rely on distribution assumptions in the reconstructed process. This restriction makes PDE methods sensitive to tackle complex instrumental broadening effect in the dispersive IR spectrometers. Also, we need to spend much time setting the parameters of PDE models manually. These problems intuitively degrade the performances of PDE methods. In this article, we propose an end-to-end neural network framework for spectral deconvolution problem. The novelty of this article lies in its strong robustness from dilated deconvolution and self-paced learning procedure to challenge the complicated degraded spectra. Actually, the deconvolution problem is tailored to a dense prediction problem in this article. Inspired by the extensive use and excellent effects of dilated convolutions in dense prediction, a lightweight dilated convolution module is given to detect the overlaps of degraded spectra. Experimental results demonstrate that the proposed solution has an outstanding performance against many other approaches. Such improvements have the potential to facilitate industrial applications and further exploration of an unknown chemical mixture. Our framework has a good performance on feature extracting and spectrum reconstruction, even in the case of low signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Detecting small targets in a complex background is always carried out by suppressing the background. The top-hat transformation is mainly utilized for background suppression in target detection. Many modified top-hat transformation methods are based on the different structures of the structural elements. However, there are two limitations. One is that the structural elements cannot sufficiently consider the contrast information between the target and surrounding area to enhance the target. Another is that structural elements should be set in advance and cannot adaptively suppress complex backgrounds. In this article, our proposed top-hat transformation is designed from two cases. First, an adaptive structural element based on a guided filter kernel is proposed for capturing the local features in infrared images for background suppression. Second, a balanced ring shape is used for two structural elements of top-hat transformation, which can utilize the contrast information between the target and background for target enhancement. More than 500 infrared target images are used in our experiment. The experimental results show that our algorithm achieves better performance in signal-to-clutter ratio gain, background suppression factor, and detection accuracy when compared with recent popular baseline methods. [ABSTRACT FROM AUTHOR]

Recently, infrared small target detection problem has attracted substantial attention. Many works based on local low-rank model have been proven to be very successful for enhancing the discriminability during detection. However, these methods construct patches by traversing local images and ignore the correlations among different patches. Although the calculation is simplified, some texture information of the target is ignored, and targets of arbitrary forms cannot be accurately identified. In this paper, a novel target-aware method based on a non-local low-rank model and saliency filter regularization is proposed, with which the newly proposed detection framework can be tailored as a non-convex optimization problem, therein enabling joint target saliency learning in a lower dimensional discriminative manifold. More specifically, non-local patch construction is applied for the proposed target-aware low-rank model. By combining similar patches, we reconstruct them together to achieve a better generalization of non-local spatial sparsity constraints. Furthermore, to encourage target saliency learning, our proposed saliency filtering regularization term based on entropy is restricted to lie between the background and foreground. The regularization of the saliency filtering locally preserves the contexts from the target and surrounding areas and avoids the deviated approximation of the low-rank matrix. Finally, a unified optimization framework is proposed and solved with the alternative direction multiplier method (ADMM). Experimental evaluations of real infrared images demonstrate that the proposed method is more robust under different complex scenes compared with some state-of-the-art methods. [ABSTRACT FROM AUTHOR]