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Demosaicking

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

Papers

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Showing 101–150 of 150 papers

TitleStatusHype
SpoC: Spoofing Camera Fingerprints—0
HighEr-Resolution Network for Image Demosaicing and EnhancingCode0
Merging-ISP: Multi-Exposure High Dynamic Range Image Signal Processing—0
Joint Demosaicing and Super-Resolution (JDSR): Network Design and Perceptual Optimization—0
Soft Prototyping Camera Designs for Car Detection Based on a Convolutional Neural NetworkCode0
Color filter array demosaicking using densely connected residual network.—0
Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing—0
CameraNet: A Two-Stage Framework for Effective Camera ISP Learning—0
Channel-by-Channel Demosaicking Networks with Embedded Spectral Correlation—0
Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw ImagesCode0
Handheld Multi-Frame Super-ResolutionCode0
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution PipelineCode0
Generating Training Data for Denoising Real RGB Images via Camera Pipeline SimulationCode0
Deep Demosaicing for Edge Implementation—0
Residual Non-local Attention Networks for Image RestorationCode0
Megapixel Photon-Counting Color Imaging using Quanta Image Sensor—0
Hue Modification Localization By Pair Matching—0
Multispectral snapshot demosaicing via non-convex matrix completion—0
CFA Bayer image sequence denoising and demosaicking chain—0
Iterative Residual CNNs for Burst Photography ApplicationsCode0
Low Cost Edge Sensing for High Quality DemosaickingCode0
Deep demosaicking for multispectral filter arrays—0
Color Image Demosaicking Using a 3-Stage Convolutional Neural Network StructureCode0
Snapshot multispectral imaging using a filter array—0
Iterative Joint Image Demosaicking and Denoising using a Residual Denoising NetworkCode0
Low Cost Edge Sensing for High Quality DemosaickingCode0
Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising NetworksCode0
Deep Residual Network for Joint Demosaicing and Super-ResolutionCode0
Joint Demosaicing and Denoising with Perceptual Optimization on a Generative Adversarial Network—0
Learning Deep Convolutional Networks for Demosaicing—0
DeepISP: Towards Learning an End-to-End Image Processing PipelineCode0
Boundary-based Image Forgery Detection by Fast Shallow CNNCode0
BLADE: Filter Learning for General Purpose Computational Photography—0
Universal Denoising Networks : A Novel CNN Architecture for Image Denoising—0
Compressed Sensing, ASBSR-method of image sampling and reconstruction and the problem of digital image acquisition with the lowest possible sampling rate—0
Consensus Convolutional Sparse CodingCode0
Deep Mean-Shift Priors for Image RestorationCode0
Bayer Demosaicking Using Optimized Mean Curvature over RGB channels—0
Reconfiguring the Imaging Pipeline for Computer VisionCode0
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging ProblemsCode0
Discriminative Transfer Learning for General Image Restoration—0
Dirty Pixels: Towards End-to-End Image Processing and PerceptionCode0
Learning Adaptive Parameter Tuning for Image Processing—0
Optimizing Codes for Source Separation in Color Image Demosaicing and Compressive Video Recovery—0
Joint Defogging and Demosaicking—0
A Framework for Fast Image Deconvolution with Incomplete ObservationsCode0
A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images—0
Generalized Inpainting Method for Hyperspectral Image Acquisition—0
Aliasing Detection and Reduction in Plenoptic Imaging—0
Demosaicing and Superresolution for Color Filter Array via Residual Image Reconstruction and Sparse Representation—0
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