Journal ArticleDOI
Comments on "Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
TLDR
Experimental results demonstrate that the proposed modification achieves better results in terms of both peak signal-to-noise ratio and subjective visual quality than the original method for strong noise.Abstract:
In order to resolve the problem that the denoising performance has a sharp drop when noise standard deviation reaches 40, proposed to replace the wavelet transform by the DCT. In this comment, we argue that this replacement is unnecessary, and that the problem can be solved by adjusting some numerical parameters. We also present this parameter modification approach here. Experimental results demonstrate that the proposed modification achieves better results in terms of both peak signal-to-noise ratio and subjective visual quality than the original method for strong noise.read more
Citations
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Proceedings ArticleDOI
Image denoising: Can plain neural networks compete with BM3D?
TL;DR: This work attempts to learn this mapping directly with a plain multi layer perceptron (MLP) applied to image patches and shows that by training on large image databases it is able to compete with the current state-of-the-art image denoising methods.
Proceedings ArticleDOI
MemNet: A Persistent Memory Network for Image Restoration
TL;DR: A very deep persistent memory network (MemNet) is proposed that introduces a memory block, consisting of a recursive unit and a gate unit, to explicitly mine persistent memory through an adaptive learning process.
Journal ArticleDOI
Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction
TL;DR: Experimental results demonstrate the state-of-the-art denoising performance of BM4D, and its effectiveness when exploited as a regularizer in volumetric data reconstruction.
Proceedings ArticleDOI
Benchmarking Denoising Algorithms with Real Photographs
Tobias Plötz,Stefan Roth +1 more
TL;DR: In this paper, the authors proposed a methodology for benchmarking denoising techniques on real photographs by capturing pairs of images with different ISO values and appropriately adjusted exposure times, where the nearly noise-free low-ISO image serves as reference.
Proceedings ArticleDOI
Image Blind Denoising with Generative Adversarial Network Based Noise Modeling
TL;DR: A novel two-step framework is proposed, in which a Generative Adversarial Network is trained to estimate the noise distribution over the input noisy images and to generate noise samples to train a deep Convolutional Neural Network for denoising.
References
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Journal ArticleDOI
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
TL;DR: An algorithm based on an enhanced sparse representation in transform domain based on a specially developed collaborative Wiener filtering achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.
BM3D Image Denoising with Shape-Adaptive Principal Component Analysis
TL;DR: This work proposes an image denoising method that ex- ploits nonlocal image modeling, principal component analysis (PCA), and local shape-adaptive anisotropic estimation and shows that the proposed method is competitive and outperforms some of the current best Denoising methods, especially in preserving image details and introducing very few artifacts.