Rotation invariant similarity measure for non-local self-similarity based image denoising

Zuo, Chenglin; Jovanov, Ljubomir; Luong, Hiêp; Goossens, Bart; Philips, Wilfried; Liu, Yu; Zhang, Maojun

doi:10.1109/icip.2015.7351074

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…Let us finally note that rotationally invariant patch comparison for image denoising was already studied in the past [41], [16], [42]. Besides that, it was noticed that denoising with regular square patches may cause noise halos around contrasted edges.…”

Section: Related Workmentioning

confidence: 95%

Affine Non-Local Means Image Denoising

Fedorov

Ballester

2017

IEEE Trans. on Image Process.

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This paper presents an extension of the Non-Local Means denoising method, that effectively exploits the affine invariant self-similarities present in the images of real scenes. Our method provides a better image denoising result by grounding on the fact that in many occasions similar patches exist in the image but have undergone a transformation. The proposal uses an affine invariant patch similarity measure that performs an appropriate patch comparison by automatically and intrinsically adapting the size and shape of the patches. As a result, more similar patches are found and appropriately used. We show that this image denoising method achieves top-tier performance in terms of PSNR, outperforming consistently the results of the regular Non-Local Means, and that it provides state-of-the-art qualitative results.

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Section: Related Workmentioning

confidence: 95%

Affine Non-Local Means Image Denoising

Fedorov

Ballester

2017

IEEE Trans. on Image Process.

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“…In the past decades, various image prior assumptions have been employed to define

R(\mathbf {X})

. The representational ones include the total variation (TV) [2–4], sparsity prior [5–8], and self‐similarity prior [9, 10]. Specifically, the TV model assumes that the gradient of image is smooth, and it is defined as the absolute difference of adjacent pixel.…”

Section: Introductionmentioning

confidence: 99%

“…Self-similarity is also extended as a regularizer, and incorporated into problem (3), such as in refs. [9,10]. The major advantages of model-based approaches are flexible and interpretable.…”

Section: Introductionmentioning

confidence: 99%

Deep side group sparse coding network for image denoising

Yin

Wang

2022

IET Image Processing

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Recently, deep learning has made significant progress in image denoising. However, most of existing deep learning based methods are purely data‐driven, without considering the knowledge of image denoising. Moreover, the parameters of deep denoising network are not explainable. According to these issues, this paper proposes a deep side group sparse coding network for image denoising, named a side group sparse coding (SGSC)‐Net. First, SGSC model for image denoising by exploiting prior information regarding the group sparse coefficients consistency is developed. Specifically, the side information is constructed as the weighted combination of intermediate estimations, and updated iteratively. Then, the optimisation solution of SGSC model is turned into a deep neural network using deep unfolding, that is, SGSC‐Net. The computational path of SGSC‐Net fully follows the iterations of optimisation solution, and consequently the network parameters are interpretable. Furthermore, the design of SGSC‐Net employs the insight of SGSC denoising model. The experimental results on well‐known datasets quantitatively and qualitatively demonstrate that SGSC‐Net is competitive to existing deep unfolding‐based and typical deep neural network‐based methods.

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“…To improve the accuracy of similarity measure, nonlocal similarity of residual image structures in method noise was further exploited in [30,31]. Besides, rotation invariant patch comparison, that can handle rotational similarity existing in the image, was also studied in [32][33][34][35][36]. Analogously in [37], affine invariant similarity measure was applied to find more similar patches.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising Using Nonlocal Means with Shape‐Adaptive Patches and New Weights

Zuo

Xiong

et al. 2021

Shock and Vibration

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Digital images captured from CMOS/CCD image sensors are prone to noise due to inherent electronic fluctuations and low photon count. To efficiently reduce the noise in the image, a novel image denoising strategy is proposed, which exploits both nonlocal self-similarity and local shape adaptation. With wavelet thresholding, the residual image in method noise, derived from the initial estimate using nonlocal means (NLM), is exploited further. By incorporating the role of both the initial estimate and the residual image, spatially adaptive patch shapes are defined, and new weights are calculated, which thus results in better denoising performance for NLM. Experimental results demonstrate that our proposed method significantly outperforms original NLM and achieves competitive denoising performance compared with state-of-the-art denoising methods.

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Rotation invariant similarity measure for non-local self-similarity based image denoising

Cited by 7 publications

References 17 publications

Affine Non-Local Means Image Denoising

Affine Non-Local Means Image Denoising

Deep side group sparse coding network for image denoising

Image Denoising Using Nonlocal Means with Shape‐Adaptive Patches and New Weights

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