Edge-preserving nonlocal weighting scheme for total variation based compressive sensing recovery

Canh, Thuong Nguyen; Dinh, Khanh Quoc

doi:10.1109/icme.2014.6890251

Cited by 16 publications

(5 citation statements)

References 12 publications

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“…where τ is the threshold value to determine the weights. When the weights are assigned 0, the pixels have large jumps and they do not contribute anything to the TV [28]. By this way, TV can focus on other pixels and the staircase artifacts can be suppressed.…”

Section: Rtv-pir Algorithm a The Rtv-pir Modelmentioning

confidence: 99%

Through-the-Wall Image Reconstruction via Reweighted Total Variation and Prior Information in Radio Tomographic Imaging

Guo

Liang

et al. 2020

IEEE Access

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This paper focuses on the application of radio tomographic imaging in through-the-wall image reconstruction. By using the simple wireless communication devices, e.g., Wi-Fi cards, the whole building layout can be reconstructed with only the received signal strength (RSS). For the reconstruction algorithms, the total variation (TV) minimization algorithm has been used to reconstruct the image. However, some false artifacts may exist in the image results due to the inherent defects of the algorithm. The artifacts can be misestimated as wall structures. In this paper, a reweighted total variation and prior information regularization algorithm named as RTV-PIR is proposed to reconstruct the image. This algorithm is based on the TV minimization of the image. And the prior information that the wall is only oriented horizontally or vertically is also considered simultaneously, which is used to keep the wall orientation. To verify the performance of the proposed algorithm, the simulations and experiments based on real data are conducted. It is shown that the RTV-PIR algorithm can get a better result with respect to the root mean square error (RMSE) and the structural similarity (SSIM), in comparison with the state-of-the-art algorithms.

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Section: Rtv-pir Algorithm a The Rtv-pir Modelmentioning

confidence: 99%

Through-the-Wall Image Reconstruction via Reweighted Total Variation and Prior Information in Radio Tomographic Imaging

Guo

Liang

et al. 2020

IEEE Access

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“…It is worth noting that edge aware weighting was recently used in [18], [19] to get better image filter result. The proposed proposed algorithm is different from all these existing papers in the sense that our method provides the first order fidelity for pixels at edges while the methods in [18], [19] only provided the zero order fidelity for the pixels at edges.…”

Section: Introductionmentioning

confidence: 99%

Content Adaptive Image Detail Enhancement

Kou

Chen

et al. 2015

IEEE Signal Process. Lett.

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Detail enhancement is required by many problems in the fields of image processing and computational photography. Existing detail enhancement algorithms first decompose a source image into a base layer and a detail layer via an edge-preserving smoothing algorithm, and then amplify the detail layer to produce a detail-enhanced image. In this letter, we propose a new norm based detail enhancement algorithm which generates the detail-enhanced image directly. The proposed algorithm preserves sharp edges better than an existing norm based algorithm. Experimental results show that the proposed algorithm reduces color distortion in the detail-enhanced image, especially around sharp edges.

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“…Zhang et al 13 use block variance to express the feature details of each block and adaptively measure the image based on the distribution of block variance. In the study by Canh et al, 14 the image edges are used as features and adaptively allocate the measurement times for each block according to varying edge. The above-mentioned works use some image features to reveal the block structure complexity, for example, block variance, edges.…”

Section: Introductionmentioning

confidence: 99%

“…However, due to different details of image blocks, the same sampling rate to measure and reconstruct each image block would cause a quality decline of block reconstruction or waste of resources. To overcome this defect, adaptive block compressive sensing (ABCS) is proposed by Zhang et al 13 and Canh et al 14 to adaptively allocate CS measurements on the basis of BCS, and to set the different measurement times for each block according to different feature details. ABCS can capture the image characteristics effectively and further improve the performance of BCS system.…”

Section: Introductionmentioning

confidence: 99%

Adaptive compressive sensing of images using error between blocks

Duan

2018

International Journal of Distributed Sensor Networks

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Block compressive sensing of image results in blocking artifacts and blurs when reconstructing images. To solve this problem, we propose an adaptive block compressive sensing framework using error between blocks. First, we divide image into several non-overlapped blocks and compute the errors between each block and its adjacent blocks. Then, the error between blocks is used to measure the structure complexity of each block, and the measurement rate of each block is adaptively determined based on the distribution of these errors. Finally, we reconstruct each block using a linear model. Experimental results show that the proposed adaptive block compressive sensing system improves the qualities of reconstructed images from both subjective and objective points of view when compared with image block compressive sensing system.

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Edge-preserving nonlocal weighting scheme for total variation based compressive sensing recovery

Cited by 16 publications

References 12 publications

Through-the-Wall Image Reconstruction via Reweighted Total Variation and Prior Information in Radio Tomographic Imaging

Through-the-Wall Image Reconstruction via Reweighted Total Variation and Prior Information in Radio Tomographic Imaging

Content Adaptive Image Detail Enhancement

Adaptive compressive sensing of images using error between blocks

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