RWRM: Residual Wasserstein regularization model for image restoration

He, Rong; Feng, Xiaoyi; Zhu, Xiaolong; Huang, Hua; Wei, Bingzhe

doi:10.3934/ipi.2020069

Cited by 3 publications

(1 citation statement)

References 42 publications

(86 reference statements)

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“…MAP (Maximum A Posteriori) algorithm is a method based on probability statistics. The basic idea is to maximize the posterior probability of HR images when LR images are known [32]. Assuming that y represents LR observation im-age and x represents the estimation of HR image, according to the MAP criterion, we can get:…”

Section: A Hr Restoration Algorithm Based On Sparse Representationmentioning

confidence: 99%

An Edge-Directed Diffusion Equation-Based Image Restoration Approach for Font Generation

Ding

2023

IEEE Access

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The intelligent algorithms-based font generation has been a prevalent online application. In existing researches, visual communication effect of font generation results is usually neglected. To deal with this issue, this paper combines sparse representation and convex set projection restoration algorithm, and proposes a font generation image restoration method based on edge Diffusion equation. Firstly, the image is modeled using sparse representation methods, and sparsity constraints are introduced to make the solution sparse and convenient for computation. Then, the Inverse problem is transformed into a Convex optimization problem, and the global optimal solution is obtained by using the properties of Convex optimization. Finally, image restoration and reconstruction are realized by edge Diffusion equation. In this paper, we propose an image restoration method for font generation based on edge Diffusion equation. By combining sparse representation and convex set projection restoration algorithms, fast image restoration and high-quality reconstruction have been achieved. And performance evaluation was conducted using the peak signal-tonoise ratio (PSNR) metric. The experimental results have well verified the correctness of this method. The experimental results show that this method can effectively solve the ill posed problem in the Inverse problem, and achieve fast image restoration and high-quality reconstruction. Compared with traditional image restoration methods, this method has higher restoration quality and faster computational speed. INDEX TERMS image reconstruction; edge-directed diffusion equation; low-resolution images; font generation

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Section: A Hr Restoration Algorithm Based On Sparse Representationmentioning

confidence: 99%

An Edge-Directed Diffusion Equation-Based Image Restoration Approach for Font Generation

Ding

2023

IEEE Access

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An alternating multiple residual Wasserstein regularization model for Gaussian image denoising

He,

Lan,

Hao

et al. 2024

Sci Rep

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Residual histograms can provide meaningful statistical information in low-level visual research. However, the existing image denoising methods do not deeply explore the potential of alternate multiple residual histograms for overall optimization constraints. Considering this deficiency, this paper presents a novel unified framework of the alternating multiple residual Wasserstein regularization model (AMRW), which can tactfully embrace multiple residual Wasserstein constraints and different image prior information for image denoising. Specifically, AMRW focuses on solving the practical and meaningful problem of restoring a clean image from multiple frame degraded images. Utilizing the Wasserstein distance in the optimal transport theory, the residual histograms of the multiple degraded images are as close as possible to the referenced Gaussian noise histogram to enhance the noise estimation accuracy. Further, the proposed concrete AMRW combines the triple residual Wasserstein distance with the image total variation prior information for Gaussian image denoising. More importantly, through the alternating implementation of residual Wasserstein regularization from different image frames, the beneficial information of the image is essentially transmitted in each cycle, continuously improving the quality of the output image. Synchronously, the alternate iterative algorithm of histogram matching and Chambolle dual projection has high implementation efficiency. AMRW provides a new research idea for other visual processing tasks such as image inpainting and image deblurring. Finally, extensive numerical experiments substantiate that our AMRW can greatly boost the subjective and objective performance of the restored images compared with some popular image denoising algorithms in recent years.

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MRWM: A Multiple Residual Wasserstein Driven Model for Image Denoising

Lan

Liu

2022

IEEE Access

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Residual histograms can provide valuable information for vision research. However, current image restoration methods have not fully exploited the potential of multiple residual histograms, especially their role as overall regularization constraints. In this paper, we propose a novel framework of multiple residual Wasserstein driven model (MRWM) that can organically combine multiple residual Wasserstein constraints and various natural image priors for image denoising. Specifically, by utilizing the Wasserstein distance derived from the optimal transmission theory, the multiple residual histograms of the observed images are forced to be as close as possible to the reference residual histogram, thereby improving the accuracy of residual estimation. Furthermore, the proposed concrete MRWM unifies the multiple residual Wasserstein distribution approximation and the image total variation prior knowledge to carry out image denoising. Alternating iterative algorithm of histogram matching and Chambolle dual projection has the characteristics of less parameters and easy implementation. Finally, our experiments confirm that compared with some representative image denoising algorithms, the MRWM can obtain better performance in objective evaluation, and can better preserve the details such as the image edges, making the image look more natural.

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RWRM: Residual Wasserstein regularization model for image restoration

Cited by 3 publications

References 42 publications

An Edge-Directed Diffusion Equation-Based Image Restoration Approach for Font Generation

An Edge-Directed Diffusion Equation-Based Image Restoration Approach for Font Generation

An alternating multiple residual Wasserstein regularization model for Gaussian image denoising

MRWM: A Multiple Residual Wasserstein Driven Model for Image Denoising

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