Computer Vision
 
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Color Image Denoising

32 papers with code • 80 benchmarks • 9 datasets

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

cszn/DnCNN • • 13 Aug 2016

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

22
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Residual Dense Network for Image Super-Resolution

yulunzhang/RDN • • CVPR 2018

In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers.

16
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Restormer: Efficient Transformer for High-Resolution Image Restoration

swz30/restormer • • CVPR 2022

Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.

13
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SwinIR: Image Restoration Using Swin Transformer

jingyunliang/swinir • • 23 Aug 2021

In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.

9
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FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising

cszn/FFDNet • • 11 Oct 2017

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

8
Paper
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Pre-Trained Image Processing Transformer

huawei-noah/Pretrained-IPT • • CVPR 2021

To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs.

6
Paper
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Real Image Denoising with Feature Attention

saeed-anwar/RIDNet • • ICCV 2019

Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, their performance is limited on real-noisy photographs and requires multiple stage network modeling.

3
Paper
Code

Improving Image Restoration by Revisiting Global Information Aggregation

megvii-research/TLC • • 8 Dec 2021

Our TLC converts global operations to local ones only during inference so that they aggregate features within local spatial regions rather than the entire large images.

3
Paper
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Learning Deep CNN Denoiser Prior for Image Restoration

cszn/ircnn • • CVPR 2017

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).

2
Paper
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MemNet: A Persistent Memory Network for Image Restoration

tyshiwo/MemNet • • ICCV 2017

We apply MemNet to three image restoration tasks, i. e., image denosing, super-resolution and JPEG deblocking.

2
Paper
Code
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