Block-Matching Convolutional Neural Network (BMCNN): Improving CNN-Based Denoising by Block-Matched Inputs

Ahn, Byeongyong; Kim, Yoon-Sik; Park, Guyong; Cho, Nam Ik

doi:10.23919/apsipa.2018.8659548

Cited by 10 publications

(10 citation statements)

References 23 publications

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“…Specifically, skip connection operation was a typically operation of signal processing [92]. For high computational cost tasks, CNN with nature of image was very effective to decrease complex [2,8,7]. For example, Ahn et al [7] used CNN with NSS to filter the noise, where similar characteristics of the given noisy image can accelerate speed of extraction feature and reduce computational cost.…”

Section: Cnn/nn and Common Feature Extraction Methods For Awni Denoisingmentioning

confidence: 99%

Deep Learning on Image Denoising: An overview

Tian

Zhang

Zheng³

et al. 2019

Preprint

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Deep learning techniques have obtained much attention in image denoising. However, deep learning methods of different types deal with the noise have enormous differences. Specifically, discriminative learning based on deep learning can well address the Gaussian noise. Optimization model methods based on deep learning have good effect on estimating of the real noise. So far, there are little related researches to summarize different deep learning techniques for image denoising. In this paper, we make such a comparative study of different deep techniques in image denoising. We first classify the (1) deep convolutional neural networks (CNNs) for additive white noisy images, (2) deep CNNs for real noisy images, (3) deep CNNs for blind denoising and (4) deep CNNs for hybrid noisy images, which is the combination of noisy, blurred and low-resolution images. Then, we analyze the motivations and principles of deep learning methods of different types. Next, we compare and verify the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analysis. Finally, we point out some potential challenges and directions of future research.

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Section: Cnn/nn and Common Feature Extraction Methods For Awni Denoisingmentioning

confidence: 99%

Deep Learning on Image Denoising: An overview

Tian

Zhang

Zheng³

et al. 2019

Preprint

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“…Block-matching can also be carried out among multi-temporal SAR images [36]. Block-matching has recently been embedded in some deep learning denoising methods [37][38][39] and despeckling methods [40,41]. However, the network involved in these methods still requires supervised training with clean references.…”

Section: Block-matching In Sar Despeckling Algorithmmentioning

confidence: 99%

SAR Image Despeckling Based on Block-Matching and Noise-Referenced Deep Learning Method

Wang

Yin

et al. 2022

Remote Sensing

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The noise2noise-based despeckling method, capable of training the despeckling deep neural network with only noisy synthetic aperture radar (SAR) image, has presented very good performance in recent research. This method requires a fine-registered multi-temporal dataset with minor time variance and uses similarity estimation to compensate for the time variance. However, constructing such a training dataset is very time-consuming and may not be viable for a certain practitioner. In this article, we propose a novel single-image-capable speckling method that combines the similarity-based block-matching and noise referenced deep learning network. The denoising network designed for this method is an encoder–decoder convolutional neural network and is accommodated to small image patches. This method firstly constructs a large number of noisy pairs as training input by similarity-based block-matching in either one noisy SAR image or multiple images. Then, the method trains the network in a Siamese manner with two parameter-sharing branches. The proposed method demonstrates favorable despeckling performance with both simulated and real SAR data with respect to other state-of-the-art reference filters. It also presents satisfying generalization capability as the trained network can despeckle well the unseen image of the same sensor. The main advantage of the proposed method is its application flexibility. It could be trained with either one noisy image or multiple images. Furthermore, the despeckling could be inferred by either the ad hoc trained network or a pre-trained one of the same sensor.

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“…Following the work in image denoising presented in [58], and extending the idea of applying autoencoders as adversarial defences [59], the BMCNN is proposed for the a method of robustifying the image recognition system against adversarial attacks. BMCNN is an attempt to merge two leading approaches to image denoising: non-local self-similarity prior based methods [60] and feed-forward denoising with the use of convolutional neural networks [61].…”

Section: Block-matching Convolutional Neural Network (Bmcnn) For Image Denoising As An Adversarial Defencementioning

confidence: 99%

Preprocessing Pipelines including Block-Matching Convolutional Neural Network for Image Denoising to Robustify Deep Reidentification against Evasion Attacks

Pawlicki

Choraś

2021

Entropy

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Artificial neural networks have become the go-to solution for computer vision tasks, including problems of the security domain. One such example comes in the form of reidentification, where deep learning can be part of the surveillance pipeline. The use case necessitates considering an adversarial setting—and neural networks have been shown to be vulnerable to a range of attacks. In this paper, the preprocessing defences against adversarial attacks are evaluated, including block-matching convolutional neural network for image denoising used as an adversarial defence. The benefit of using preprocessing defences comes from the fact that it does not require the effort of retraining the classifier, which, in computer vision problems, is a computationally heavy task. The defences are tested in a real-life-like scenario of using a pre-trained, widely available neural network architecture adapted to a specific task with the use of transfer learning. Multiple preprocessing pipelines are tested and the results are promising.

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Block-Matching Convolutional Neural Network (BMCNN): Improving CNN-Based Denoising by Block-Matched Inputs

Cited by 10 publications

References 23 publications

Deep Learning on Image Denoising: An overview

Deep Learning on Image Denoising: An overview

SAR Image Despeckling Based on Block-Matching and Noise-Referenced Deep Learning Method

Preprocessing Pipelines including Block-Matching Convolutional Neural Network for Image Denoising to Robustify Deep Reidentification against Evasion Attacks

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