Demosaicking
55 papers with code • 0 benchmarks • 1 datasets
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
These leaderboards are used to track progress in Demosaicking
PixelShift200
Most implemented papers
Color Image Demosaicking Using a 3-Stage Convolutional Neural Network Structure
Color demosaicking (CDM) is a critical first step for the acquisition of high-quality RGB images with single chip cameras.
Low Cost Edge Sensing for High Quality Demosaicking
Compared with the methods of similar computational cost, our method achieves substantially higher accuracy, whereas compared with the methods of similar accuracy, our method has significantly lower cost.
Iterative Residual CNNs for Burst Photography Applications
cig-skoltech/burst-cvpr-2019
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CVPR 2019
In this work, we focus on the fact that every frame of a burst sequence can be accurately described by a forward (physical) model.
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.
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline
In this work, we comprehensively study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions.
Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw Images
tehret/mosaic-to-mosaic
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ICCV 2019
Due to the unavailability of ground truth data these networks cannot be currently trained using real RAW images.
Soft Prototyping Camera Designs for Car Detection Based on a Convolutional Neural Network
It is better to evaluate camera designs for CNN applications using soft prototyping with task-specific metrics rather than consumer photography metrics.
HighEr-Resolution Network for Image Demosaicing and Enhancing
However, plenty of studies have shown that global information is crucial for image restoration tasks like image demosaicing and enhancing.
Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration
bruno-31/groupsc
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ECCV 2020
Non-local self-similarity and sparsity principles have proven to be powerful priors for natural image modeling.
Moire Image Restoration using Multi Level Hyper Vision Net
sabaridsn/MultilevelHyper_Vision_Net
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Inspired by these challenges in demoireing, a multilevel hyper vision net is proposed to remove the Moire pattern to improve the quality of the images.
