Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising

Cruz, Cristóvão; Foi, Alessandro; Katkovnik, Vladimir; Егиазарян, Карен

doi:10.1109/lsp.2018.2850222

Cited by 107 publications

(55 citation statements)

References 18 publications

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“…The authors train the linear transformations and the shrinkage function. In [15] the authors propose an iterative approach that can be used to reinforce non-locality to any denoiser. Each iteration consists of the application of the denoiser followed by a non-local filtering step using a fixed image (denoised with BM3D) for computing the non-local correspondences.…”

Section: Introductionmentioning

confidence: 99%

A Non-Local CNN for Video Denoising

Davy

Ehret

Morel

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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Non-local patch based methods were until recently stateof-the-art for image denoising but are now outperformed by CNNs. Yet they are still the state-of-the-art for video denoising, as video redundancy is a key factor to attain high denoising performance. The problem is that CNN architectures are hardly compatible with the search for selfsimilarities. In this work we propose a new and efficient way to feed video self-similarities to a CNN. The nonlocality is incorporated into the network via a first nontrainable layer which finds for each patch in the input image its most similar patches in a search region. The central values of these patches are then gathered in a feature vector which is assigned to each image pixel. This information is presented to a CNN which is trained to predict the clean image. We apply the proposed architecture to image and video denoising. For the latter patches are searched for in a 3D spatio-temporal volume. The proposed architecture achieves state-of-the-art results. To the best of our knowledge, this is the first successful application of a CNN to video denoising.1

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Section: Introductionmentioning

confidence: 99%

A Non-Local CNN for Video Denoising

Davy

Ehret

Morel

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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“…The use of a CNN for image denoising can be tracked back to [103], where a five-layer network was developed. In recent years, many CNN-based denoising methods have been proposed [99,[104][105][106][107][108]. Compared to that of ref.…”

Section: Cnn-based Denoising Methodsmentioning

confidence: 99%

Brief review of image denoising techniques

Fan

Zhang²,

Fan³

et al. 2019

Vis. Comput. Ind. Biomed. Art

435

220

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With the explosion in the number of digital images taken every day, the demand for more accurate and visually pleasing images is increasing. However, the images captured by modern cameras are inevitably degraded by noise, which leads to deteriorated visual image quality. Therefore, work is required to reduce noise without losing image features (edges, corners, and other sharp structures). So far, researchers have already proposed various methods for decreasing noise. Each method has its own advantages and disadvantages. In this paper, we summarize some important research in the field of image denoising. First, we give the formulation of the image denoising problem, and then we present several image denoising techniques. In addition, we discuss the characteristics of these techniques. Finally, we provide several promising directions for future research.

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“…To the best of our knowledge, the interplay between nonlocal methods and deep learning for SAR despeckling has been first explored in two very recent papers. In [32], the approach of Cruz et al [29] is followed, in which nonlocal processing is used to refine the output of CNN-based filters. Instead, in [31], we proposed to use nonlocal means filtering with weights computed patch-by-patch by means of a dedicated CNN, so as to compare the weights provided by the network with those output by conventional nonlocal methods.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, we try to blend the nonlocal concept with CNN-based image processing, with the aim of exploiting their complementary strengths for SAR despeckling. Although some CNN-based nonlocal methods have been proposed for AWGN denoising, in the last few years (e.g., [27][28][29][30]), only very recently, researchers have begun to explore this promising approach for SAR despeckling [31,32]. In particular, here we follow our recent conference paper [31], and propose a simple CNN-powered nonlocal means filter, that is, plain pixel-wise nonlocal means in which the filter weights are computed by means of a dedicated convolutional network.…”

Section: Introductionmentioning

confidence: 99%

Nonlocal CNN SAR Image Despeckling

et al. 2020

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We propose a new method for SAR image despeckling, which performs nonlocal filtering with a deep learning engine. Nonlocal filtering has proven very effective for SAR despeckling. The key idea is to exploit image self-similarities to estimate the hidden signal. In its simplest form, pixel-wise nonlocal means, the target pixel is estimated through a weighted average of neighbors, with weights chosen on the basis of a patch-wise measure of similarity. Here, we keep the very same structure of plain nonlocal means, to ensure interpretability of results, but use a convolutional neural network to assign weights to estimators. Suitable nonlocal layers are used in the network to take into account information in a large analysis window. Experiments on both simulated and real-world SAR images show that the proposed method exhibits state-of-the-art performance. In addition, the comparison of weights generated by conventional and deep learning-based nonlocal means provides new insight into the potential and limits of nonlocal information for SAR despeckling.

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Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising

Cited by 107 publications

References 18 publications

A Non-Local CNN for Video Denoising

A Non-Local CNN for Video Denoising

Brief review of image denoising techniques

Nonlocal CNN SAR Image Despeckling

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