Demosaicking Edit Task
Computer Vision • Image Restoration
About
Most modern digital cameras acquire color images by measuring only one color channel per pixel, red, green, or blue, according to a specific pattern called the Bayer pattern. Demosaicking is the processing step that reconstruct a full color image given these incomplete measurements.
Source: Revisiting Non Local Sparse Models for Image Restoration
Benchmarks
Datasets
Greatest papers with code
Residual Non-local Attention Networks for Image Restoration
To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts.
Pyramid Attention Networks for Image Restoration
• SHI-Labs/Pyramid-Attention-Networks
• 
Self-similarity refers to the image prior widely used in image restoration algorithms that small but similar patterns tend to occur at different locations and scales.
Replacing Mobile Camera ISP with a Single Deep Learning Model
• aiff22/pynet
• 
The model is trained to convert RAW Bayer data obtained directly from mobile camera sensor into photos captured with a professional high-end DSLR camera, making the solution independent of any particular mobile ISP implementation.
Handheld Multi-Frame Super-Resolution
In this paper, we supplant the use of traditional demosaicing in single-frame and burst photography pipelines with a multiframe super-resolution algorithm that creates a complete RGB image directly from a burst of CFA raw images.
Trinity of Pixel Enhancement: a Joint Solution for Demosaicking, Denoising and Super-Resolution
Despite the recent improvement of learning-based image processing methods in image quality, there lacks enough analysis into their interactions and characteristics under a realistic setting of the mixture problem of demosaicing, denoising and SR.
Plug-and-Play Image Restoration with Deep Denoiser Prior
• cszn/DPIR
•
Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems.
Iterative Joint Image Demosaicking and Denoising using a Residual Denoising Network
• cig-skoltech/deep_demosaick
•
Modern approaches try to jointly solve these problems, i. e. joint denoising-demosaicking which is an inherently ill-posed problem given that two-thirds of the intensity information is missing and the rest are perturbed by noise.
Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks
ECCV 2018
• cig-skoltech/deep_demosaick
•
Demosaicking and denoising are among the most crucial steps of modern digital camera pipelines and their joint treatment is a highly ill-posed inverse problem where at-least two-thirds of the information are missing and the rest are corrupted by noise.
CURL: Neural Curve Layers for Global Image Enhancement
• sjmoran/CURL
•
We present a novel approach to adjust global image properties such as colour, saturation, and luminance using human-interpretable image enhancement curves, inspired by the Photoshop curves tool.
Ranked #1 on
Image Enhancement
on MIT-Adobe 5k
Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation
Unfortunately, the commonly used additive white noise (AWGN) models do not accurately reproduce the noise and the degradation encountered on these inputs.