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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 51–100 of 150 papers

TitleStatusHype
Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot—0
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
Del-Net: A Single-Stage Network for Mobile Camera ISP—0
Demosaicing and Superresolution for Color Filter Array via Residual Image Reconstruction and Sparse Representation—0
Discriminative Transfer Learning for General Image Restoration—0
Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures—0
Learning Deep Convolutional Networks for Demosaicing—0
Learning Degradation-Independent Representations for Camera ISP Pipelines—0
Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization—0
Learning to Jointly Deblur, Demosaick and Denoise Raw Images—0
SDAT: Sub-Dataset Alternation Training for Improved Image Demosaicing—0
MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging—0
Megapixel Photon-Counting Color Imaging using Quanta Image Sensor—0
Merging-ISP: Multi-Exposure High Dynamic Range Image Signal Processing—0
Model-based demosaicking for acquisitions by a RGBW color filter array—0
Monochrome and Color Polarization Demosaicking Using Edge-Aware Residual Interpolation—0
MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing—0
Multispectral snapshot demosaicing via non-convex matrix completion—0
Multispectral Demosaicing via Dual Cameras—0
NeRD: Neural field-based Demosaicking—0
Neural Network Generalization: The impact of camera parameters—0
NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge—0
Optimizing Codes for Source Separation in Color Image Demosaicing and Compressive Video Recovery—0
Pixel-Inconsistency Modeling for Image Manipulation Localization—0
Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration—0
Pre-demosaic Graph-based Light Field Image Compression—0
Procedural Kernel Networks—0
Raw Bayer Pattern Image Synthesis for Computer Vision-oriented Image Signal Processing Pipeline Design—0
Real-time division-of-focal-plane polarization imaging system with progressive networks—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Joint Demosaicking and Denoising Benefits from a Two-stage Training Strategy—0
Residual Swin Transformer Channel Attention Network for Image Demosaicing—0
Seeing like a Cephalopod: Colour Vision with a Monochrome Event Camera—0
Sign-Coded Exposure Sensing for Noise-Robust High-Speed Imaging—0
Snapshot HDR Video Construction Using Coded Mask—0
Snapshot multispectral imaging using a filter array—0
Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: Application to surgical imaging—0
SpoC: Spoofing Camera Fingerprints—0
ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing—0
Toward Moiré-Free and Detail-Preserving Demosaicking—0
Double Binnable RGB, RGBW and LMS Color Filter Arrays—0
Two-Step Color-Polarization Demosaicking Network—0
Universal Denoising Networks : A Novel CNN Architecture for Image Denoising—0
Unsupervised Spectral Demosaicing with Lightweight Spectral Attention Networks—0
Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution—0
Generalized Inpainting Method for Hyperspectral Image Acquisition—0
Generative Model-Assisted Demosaicing for Cross-multispectral Cameras—0
Gradient-based Feature Extraction From Raw Bayer Pattern Images—0
How to Best Combine Demosaicing and Denoising?—0
Hue Modification Localization By Pair Matching—0
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