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Image Denoising

63 papers with code · Computer Vision
Subtask of Denoising

Image Denoising is the task of removing noise from an image, e.g. the application of Gaussian noise to an image.

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Greatest papers with code

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

13 Aug 2016cszn/DnCNN

Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.

IMAGE DENOISING IMAGE SUPER-RESOLUTION

Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections

29 Jun 2016titu1994/Image-Super-Resolution

In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers.

IMAGE DENOISING IMAGE RESTORATION SUPER RESOLUTION

Learning Deep CNN Denoiser Prior for Image Restoration

CVPR 2017 cszn/ircnn

Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e. g., deblurring).

DEBLURRING IMAGE DENOISING IMAGE RESTORATION

Toward Convolutional Blind Denoising of Real Photographs

CVPR 2019 GuoShi28/CBDNet

While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs.

IMAGE DENOISING

Neural Nearest Neighbors Networks

NeurIPS 2018 visinf/n3net

To exploit our relaxation, we propose the neural nearest neighbors block (N3 block), a novel non-local processing layer that leverages the principle of self-similarity and can be used as building block in modern neural network architectures.

IMAGE DENOISING IMAGE RESTORATION IMAGE SUPER-RESOLUTION

FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising

11 Oct 2017cszn/FFDNet

Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising.

IMAGE DENOISING

Non-Local Recurrent Network for Image Restoration

NeurIPS 2018 Ding-Liu/NLRN

The main contributions of this work are: (1) Unlike existing methods that measure self-similarity in an isolated manner, the proposed non-local module can be flexibly integrated into existing deep networks for end-to-end training to capture deep feature correlation between each location and its neighborhood.

IMAGE DENOISING IMAGE RESTORATION IMAGE SUPER-RESOLUTION

Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers

CVPR 2019 hejingwenhejingwen/AdaFM

In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods.

IMAGE DENOISING IMAGE RESTORATION IMAGE SUPER-RESOLUTION

Modular proximal optimization for multidimensional total-variation regularization

3 Nov 2014albarji/proxTV

We study \emph{TV regularization}, a widely used technique for eliciting structured sparsity.

IMAGE DECONVOLUTION IMAGE DENOISING VIDEO DENOISING