Infrared Image Super-Resolution Reconstruction Based on Quaternion Fractional Order Total Variation with Lp Quasinorm

Liu, Xingguo; Chen, Yingpin; Peng, Zhenming; Wu, Juan; Wang, Zhuoran

doi:10.3390/app8101864

Cited by 28 publications

(19 citation statements)

References 45 publications

(53 reference statements)

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“…Among them, the sparse and low-rank matrices recovery-based method has received much attention. However, since such methods usually use the L1-norm as an approximation of the L0-norm, the result may fall into the local minimum rather than the global minimum [42], which affects the constraints of the sparse item; consequently, the detection result is mixed with clutter, and the detection algorithm is poorly robust. Fortunately, there is still much room for improvement in the design of methods.…”

Section: Motivationmentioning

confidence: 99%

Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

et al. 2019

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The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on non-convex optimization with Lp-norm constraint (NOLC). The NOLC method strengthens the sparse item constraint with Lp-norm while appropriately scaling the constraints on low-rank item, so the NP-hard problem is transformed into a non-convex optimization problem. First, the infrared image is converted into a patch image and is secondly solved by the alternating direction method of multipliers (ADMM). In this paper, an efficient solver is given by improving the convergence strategy. The experiment shows that NOLC can accurately detect the target and greatly suppress the background, and the advantages of the NOLC method in detection efficiency and computational efficiency are verified.

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Section: Motivationmentioning

confidence: 99%

Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

et al. 2019

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“…The size of dim targets is commonly less than 80 pixels in the imaging plane. Also, there are no fixed shapes and textures [1] for the infrared dim targets. Moreover, the infrared dim targets are often interfered with by non-uniform backgrounds, such as houses, trees, and clouds [2,3].…”

Section: Introductionmentioning

confidence: 99%

Infrared Dim Target Detection Using Shearlet’s Kurtosis Maximization under Non-Uniform Background

et al. 2019

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A novel method based on multiscale and multidirectional feature fusion in the shearlet transform domain and kurtosis maximization for detecting the dim target in infrared images with a low signal-to-noise ratio (SNR) and serious interference caused by a cluttered and non-uniform background is presented in this paper. First, an original image is decomposed using the shearlet transform with translation invariance. Second, various directions of high-frequency subbands are fused and the corresponding kurtosis of fused image is computed. The targets can be enhanced by strengthening the column with maximum kurtosis. Then, processed high-frequency subbands on different scales of images are merged. Finally, the dim targets are detected by an adaptive threshold with a maximum contrast criterion (MCC). The experimental results show that the proposed method has good performance for infrared target detection in comparison with the nonsubsampled contourlet transform (NSCT) method.

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“…To solve this problem, many methods were proposed in recent processing a scene with strong edges [38]. Although the following methods [39][40][41][42][43] obtain a remarkable progress to remove the edge residuals, they can hardly eliminate the strong local clutters of various shapes completely by employing a specific sophisticated norm to replace the nuclear norm.…”

Section: Introductionmentioning

confidence: 99%

Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

Huang

Liu

et al. 2019

Remote Sensing

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Robust detection of infrared small target is an important and challenging task in many photoelectric detection systems. Using the difference of a specific feature between the target and the background, various detection methods were proposed in recent decades. However, most methods extract the feature in a region with fixed shape, especially in a rectangular region, which causes a problem: when faced with complex-shape clutters, the rectangular region involves the pixels inside and outside the clutters, and the significant grey-level difference among these pixels leads to a relatively large feature in the clutter area, interfering with the target detection. In this paper, we propose a structure-adaptive clutter suppression method, called chain-growth filtering, for robust infrared small target detection. The well-designed filtering model can adjust its shape to fit various clutter structures such as lines, curves and irregular edges, and thus has a more robust clutter suppression capability than the fixed-shape feature extraction strategy. In addition, the proposed method achieves a considerable anti-noise ability by employing guided filter as a preprocessing approach and enjoys the capability of multi-scale target detection without complex parameter tuning. In the experiment, we evaluate the performance of the detection method through 12 typical infrared scenes which contain different types of clutters. Compared with seven state-of-the-art methods, the proposed method shows the superior clutter-suppression effects for various types of clutters and the excellent detection performance for various scenes.Remote Sens. 2020, 12, 47 2 of 22 decades, and they can be generally grouped into two categories: track-before-detect (TBD)-based methods and detect-before-track (DBT)-based methods.The TBD-based methods, such as pipeline filtering [11], hypothesis testing [12], 3-D matched filtering [13], temporal profile filtering [14], dynamic programming [15] and so on, try to use the grayscale consistency and the trajectory continuity of the targets in consecutive frames so as to discriminate the small targets from noise [16][17][18][19]. These methods are based on two assumptions; one is that the motion model of the target is known, the other is that the background motion is slow. In ideal conditions, both assumptions are satisfied; the energy of the targets is accumulated in adjacent frames, and the difference between targets and noise is increased. As a result, the TBD-based methods perform well in low signal-to-clutter conditions. However, in practical applications, we can hardly acquire the precise motion model of the targets, and the backgrounds can move fast when the infrared detector is in a moving platform. Therefore, both of the two assumptions could fail in real situations, and the performance of the TBD-based methods could degrade significantly [20]. Meanwhile, high time and storage requirements also make these TBD approaches unsuited to large-scale engineering projects [21].Compared with TBD-based methods, DBT-bas...

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Infrared Image Super-Resolution Reconstruction Based on Quaternion Fractional Order Total Variation with Lp Quasinorm

Cited by 28 publications

References 45 publications

Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

Infrared Dim Target Detection Using Shearlet’s Kurtosis Maximization under Non-Uniform Background

Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

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