# Color Image Denoising

18 papers with code • 47 benchmarks • 8 datasets

## Most implemented papers

# Residual Dense Network for Image Super-Resolution

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.

# Restormer: Efficient Transformer for High-Resolution Image Restoration

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.

# SwinIR: Image Restoration Using Swin Transformer

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.

# Real Image Denoising with Feature Attention

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.

# Pre-Trained Image Processing Transformer

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.

# Learning Deep CNN Denoiser Prior for Image Restoration

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).

# MemNet: A Persistent Memory Network for Image Restoration

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

# Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning

Blind and universal image denoising consists of using a unique model that denoises images with any level of noise.

# RENOIR - A Dataset for Real Low-Light Image Noise Reduction

Image denoising algorithms are evaluated using images corrupted by artificial noise, which may lead to incorrect conclusions about their performances on real noise.

# Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies

Convex relaxations of nonconvex multilabel problems have been demonstrated to produce superior (provably optimal or near-optimal) solutions to a variety of classical computer vision problems.