An edge driven wavelet frame model for image restoration

Choi, Jae Kyu; Dong, Bin; Zhang, Xiaoqun

doi:10.1016/j.acha.2018.09.007

Cited by 18 publications

(7 citation statements)

References 56 publications

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“…Thus, the reconstruction quality of this method needs improvement. Wavelet frame method is able to detect boundary information at different scales and enforce sparseness of the solution (Choi et al, 2020; Wang et al, 2021). To improve the image quality reconstructed by Tikhonov regularization method, a novel image reconstruction approach which combines this method with a wavelet frame is proposed in this work.…”

Section: Image Reconstruction Based On the Proposed Methodsmentioning

confidence: 99%

Reconstruction of conductivity distribution variation with Tikhonov and wavelet frame combined method for electrical impedance tomography

Shi

Yuhang

Wang

et al. 2023

Transactions of the Institute of Measurement and Control

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Electrical impedance tomography (EIT) is receiving considerable research interest in a variety of applications. With this technique, it is possible to reconstruct conductivity distribution inside the detected region. Note that reconstruction of conductivity distribution is an ill-posed inverse problem. To deal with this problem, Tikhonov regularization method has been preferred. However, this method suffers from poor reconstruction quality. In this work, a novel approach which combines Tikhonov regularization method with wavelet frame is proposed for image reconstruction of EIT. The proposed method is superior to the popular Tikhonov regularization method in enforcing sparse of the solution and enhancing sharp feature. Simulation work has been conducted to validate the performance of the proposed method. Compared with Landweber method, total variation (TV) regularization method, and Tikhonov regularization method, reconstructions of six different models show that images reconstructed by the proposed method have better quality and are more robust to noise. In addition, image reconstruction is also performed based on phantom experimental data. The results further demonstrate that the proposed method outperforms other three methods since the inclusion has been more accurately reconstructed, and there are almost no artifacts in the reconstructed images.

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Section: Image Reconstruction Based On the Proposed Methodsmentioning

confidence: 99%

Reconstruction of conductivity distribution variation with Tikhonov and wavelet frame combined method for electrical impedance tomography

Shi

Yuhang

Wang

et al. 2023

Transactions of the Institute of Measurement and Control

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show abstract

“…Similar idea is considered in [39] that borrows minimax concave penalty (MCP) [40] from compressed sensing to image processing. Other than gradient-based regularizations, wavelet frame based approaches are also widely used due to its multiresolution structure, sparse representations, and high redundancy [41][42][43][44][45][46][47]. Note that there are several different types of wavelet models, including synthesis based approaches [48,49], analysis based approaches [50,51], and balanced approaches [52,53].…”

Section: Regularization Methodsmentioning

confidence: 99%

An image sharpening operator combined with framelet for image deblurring

Liu¹,

Lou²,

Ni³

et al. 2020

Inverse Problems

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Image sharpening can highlight fine details in images but with a tendency of amplifying noise. This paper proposes a novel idea of incorporating an image sharpening operator into a framelet-based model for image deblurring. The proposed model is convex and hence it can be solved efficiently by the semiproximal alternating direction method of multipliers (sPADMM) with guaranteed linear rate convergence, which covers the classical ADMM. The experimental results on different blurring kernels and Gaussian noise levels show that the proposed approach outperforms the state-of-the-art methods in terms of PSNR, SSIM, relative error, and visual quality.

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“…In case of the data with different scales, construction of a low-rank tensor is needed, so Lu et al [16] proposed a model based on multi-band filters which are guided using low-rank tensor. An edge detection based on wavelet frame model is introduced by Choi et al [17] to conduct image restoration by assuming that the image is a smooth function. Motohashi et al [18] proposed a new method based on blind deconvolution to estimate the point spread function and improve the performance of restoration.…”

Section: Introductionmentioning

confidence: 99%

Image Restoration by Projection onto Convex Sets with Particle Swarm Parameter Optimization

Rashno

Fadaei

2023

IJE

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Image restoration is the operation of obtaining a high-quality image from a corrupt/noisy image and is widely used in many applications such as Magnetic Resonance Imaging (MRI) and fingerprint identification. This paper proposes an image restoration model based on projection onto convex sets (POCS) and particle swarm optimization (PSO). For this task, a number of convex sets are used as constraints and images are projected to these sets iteratively to reach restored image. Since relaxation parameter in POCS has a significant effect on restoration results, PSO is developed to find the best value for this parameter to be used in restoration process. The proposed scheme for image restoration is evaluated on three popular images with 4 configurations of noise, compared with 5 competitive restoration models. Results demonstrate that the proposed method outperforms other models in 32 out of 48 cases in images with different noise configurations with respect to relative error, ISNR, MAE and MSE measures.

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An edge driven wavelet frame model for image restoration

Cited by 18 publications

References 56 publications

Reconstruction of conductivity distribution variation with Tikhonov and wavelet frame combined method for electrical impedance tomography

Reconstruction of conductivity distribution variation with Tikhonov and wavelet frame combined method for electrical impedance tomography

An image sharpening operator combined with framelet for image deblurring

Image Restoration by Projection onto Convex Sets with Particle Swarm Parameter Optimization

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