scholarly journals Infrared Image Super-Resolution Reconstruction Based on Quaternion and High-Order Overlapping Group Sparse Total Variation

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5139 ◽  
Author(s):  
Xingguo Liu ◽  
Yingpin Chen ◽  
Zhenming Peng ◽  
Juan Wu
Keyword(s):  
Total Variation ◽  
Super Resolution ◽  
High Order ◽  
Practical Significance ◽  
Variation Method ◽  
Reconstruction Method ◽  
Infrared Images ◽  
Denoising Method ◽  
Image Structure ◽  
Imaging Characteristics

Owing to the limitations of imaging principles and system imaging characteristics, infrared images generally have some shortcomings, such as low resolution, insufficient details, and blurred edges. Therefore, it is of practical significance to improve the quality of infrared images. To make full use of the information on adjacent points, preserve the image structure, and avoid staircase artifacts, this paper proposes a super-resolution reconstruction method for infrared images based on quaternion total variation and high-order overlapping group sparse. The method uses a quaternion total variation method to utilize the correlation between adjacent points to improve image anti-noise ability and reconstruction effect. It uses the sparsity of a higher-order gradient to reconstruct a clear image structure and restore smooth changes. In addition, we performed regularization by using the denoising method, alternating direction method of multipliers, and fast Fourier transform theory to improve the efficiency and robustness of our method. Our experimental results show that this method has excellent performance in objective evaluation and subjective visual effects.

High order total variation method for interior tomography

2012 ◽  
Author(s):  
Jiansheng Yang ◽  
Hengyong Yu ◽  
Wenxiang Cong ◽  
Ming Jiang ◽  
Ge Wang
Keyword(s):  
Total Variation ◽  
High Order ◽  
Variation Method

scholarly journals Fast Total Variation Method Based on Iterative Reweighted Norm for Airborne Scanning Radar Super-Resolution Imaging

2020 ◽  
Vol 12 (18) ◽  
pp. 2877
Author(s):  
Xingyu Tuo ◽  
Yin Zhang ◽  
Yulin Huang ◽  
Jianyu Yang
Keyword(s):  
Total Variation ◽  
Structural Characteristics ◽  
Super Resolution ◽  
Variation Method ◽  
High Dimensional ◽  
Imaging Method ◽  
Lp Norm ◽  
L2 Norm ◽  
The Matrix ◽  
Resolution Imaging

The total variation (TV) method has been applied to realizing airborne scanning radar super-resolution imaging while maintaining the outline of the target. The iterative reweighted norm (IRN) approach is an algorithm for addressing the minimum Lp norm problem by solving a sequence of minimum weighted L2 norm problems, and has been applied to solving the TV norm. However, during the solving process, the IRN method is required to update the weight term and result term in each iteration, involving multiplications and the inversion of large matrices. Consequently, it suffers from a huge calculation load, which seriously restricts the application of the TV imaging method. In this work, by analyzing the structural characteristics of the matrix involved in iteration, an efficient method based on suitable matrix blocking is proposed. It transforms multiplications and the inversion of large matrices into the computation of multiple small matrices, thereby accelerating the algorithm. The proposed method, called IRN-FTV method, is more time economical than the IRN-TV method, especially for high dimensional observation scenarios. Numerical results illustrate that the proposed IRN-FTV method enjoys preferable computational efficiency without performance degradation.

scholarly journals Airborne Radar Super-Resolution Imaging Based on Fast Total Variation Method

2021 ◽  
Vol 13 (4) ◽  
pp. 549
Author(s):  
Qiping Zhang ◽  
Yin Zhang ◽  
Yongchao Zhang ◽  
Yulin Huang ◽  
Jianyu Yang
Keyword(s):  
Computational Complexity ◽  
Total Variation ◽  
Super Resolution ◽  
Real Data ◽  
Radar Imaging ◽  
Matrix Inversion ◽  
Variation Method ◽  
Airborne Radar ◽  
The Matrix ◽  
Very High

Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging. However, the computational complexity is very high because of the matrix inversion, reaching O(N3). In this paper, a Gohberg–Semencul (GS) representation based fast TV (GSFTV) method is proposed to make up for the shortcoming. The proposed GSFTV method fist utilizes a one-dimensional TV norm as the regular term under regularization framework, which is conducive to achieve super-resolution while preserving the target contour. Then, aiming at the very high computational complexity caused by matrix inversion when minimizing the TV regularization problem, we use the low displacement rank feature of Toeplitz matrix to achieve fast inversion through GS representation. This reduces the computational complexity from O(N3) to O(N2), benefiting efficiency improvement for airborne radar imaging. Finally, the simulation and real data processing results demonstrate that the proposed GSFTV method can simultaneously improve the resolution and preserve the target contour. Moreover, the very high computational efficiency of the proposed GSFTV method is tested by hardware platform.

Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images

2020 ◽  
Vol 13 (4) ◽  
pp. 14-31
Author(s):  
Nikita Joshi ◽  
Sarika Jain ◽  
Amit Agarwal
Keyword(s):  
Magnetic Resonance ◽  
Total Variation ◽  
Similarity Index ◽  
Noise Removal ◽  
Magnetic Resonance Images ◽  
Variation Method ◽  
Mr Images ◽  
Denoising Method ◽  
Local Means ◽  
Non Local

Magnetic resonance (MR) images suffer from noise introduced by various sources. Due to this noise, diagnosis remains inaccurate. Thus, removal of noise becomes a very important task when dealing with MR images. In this paper, a denoising method has been discussed that makes use of non-local means filter and discrete total variation method. The proposed approach has been compared with other noise removal techniques like non-local means filter, anisotropic diffusion, total variation, and discrete total variation method, and it proves to be effective in reducing noise. The performance of various denoising methods is compared on basis of metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), universal image quality index (UQI), and structure similarity index (SSIM) values. This method has been tested for various noise levels, and it outperformed other existing noise removal techniques, without blurring the image.

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

2018 ◽  
Vol 8 (10) ◽  
pp. 1864 ◽  
Author(s):  
Xingguo Liu ◽  
Yingpin Chen ◽  
Zhenming Peng ◽  
Juan Wu ◽  
Zhuoran Wang
Keyword(s):  
Total Variation ◽  
Fractional Order ◽  
Infrared Image ◽  
Super Resolution ◽  
Objective Evaluation ◽  
Imaging Systems ◽  
Infrared Images ◽  
Alternating Direction ◽  
Image Super Resolution ◽  
Staircase Artifacts

Owing to the limitations of the imaging principle as well as the properties of imaging systems, infrared images often have some drawbacks, including low resolution, a lack of detail, and indistinct edges. Therefore, it is essential to improve infrared image quality. Considering the information of neighbors, a description of sparse edges, and by avoiding staircase artifacts, a new super-resolution reconstruction (SRR) method is proposed for infrared images, which is based on fractional order total variation (FTV) with quaternion total variation and the L p quasinorm. Our proposed method improves the sparsity exploitation of FTV, and efficiently preserves image structures. Furthermore, we adopt the plug-and-play alternating direction method of multipliers (ADMM) and the fast Fourier transform (FFT) theory for the proposed method to improve the efficiency and robustness of our algorithm; in addition, an accelerated step is adopted. Our experimental results show that the proposed method leads to excellent performances in terms of an objective evaluation and the subjective visual effect.

Image Denoising New Method Based on Fractional Partial Differential Equation

2012 ◽  
Vol 532-533 ◽  
pp. 797-802 ◽  
Author(s):  
Wei Jiang ◽  
Zheng Xia Wang
Keyword(s):  
Image Denoising ◽  
Total Variation ◽  
Fractional Order ◽  
New Method ◽  
Variation Method ◽  
Partial Differential ◽  
Denoising Method ◽  
Characteristics Of Image ◽  
Image Edges ◽  
The One

Current total variation method excels at denoising and keeping the characteristics of image edges. However, its ability to retain texture details of smoothing region of image is poor. By combining fractional-order differential theory with total variation method, a new image denoising method is proposed. The new method, while effectively inheriting these advantages, uses the fractional-order differential amplitude-frequency and effectively. Simulation results which we have got show that the new method, on the one hand, can better suppress noise, keep the characteristics of image edges, and retain more texture details than integer-order partial differential methods. On the other hand, the method, above mentioned, is more effective and practical on image denoising than results of PSNR.

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