Color Image Denoising
27 papers with code • 61 benchmarks • 8 datasets
Datasets
Latest papers
Patch Craft: Video Denoising by Deep Modeling and Patch Matching
Our algorithm augments video sequences with patch-craft frames and feeds them to a CNN.
Color Image Restoration Exploiting Inter-channel Correlation with a 3-stage CNN
We demonstrate the capabilities of the proposed 3-stage structure with three typical color image restoration tasks: color image demosaicking, color compression artifacts reduction, and real-world color 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.
Rank-One Network: An Effective Framework for Image Restoration
The RO decomposition is developed to decompose a corrupted image into the RO components and residual.
Rethinking the CSC Model for Natural Images
Sparse representation with respect to an overcomplete dictionary is often used when regularizing inverse problems in signal and image processing.
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.
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.
A Brief Review of Real-World Color Image Denoising
Filtering real-world color images is challenging due to the complexity of noise that can not be formulated as a certain distribution.
Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising
In this work, we propose a new method for natural image denoising that trains a deep neural network to determine whether patches in a noisy image input share common underlying patterns.
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.