Color Image Denoising
24 papers with code • 50 benchmarks • 9 datasets
These leaderboards are used to track progress in Color Image Denoising
Most implemented papers
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
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.
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising.
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.
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.
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.