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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
Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems—0
Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors—0
Unsupervised Spectral Demosaicing with Lightweight Spectral Attention Networks—0
Learning Degradation-Independent Representations for Camera ISP Pipelines—0
Double Binnable RGB, RGBW and LMS Color Filter Arrays—0
Model-based demosaicking for acquisitions by a RGBW color filter array—0
ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing—0
Toward Moiré-Free and Detail-Preserving Demosaicking—0
Sign-Coded Exposure Sensing for Noise-Robust High-Speed Imaging—0
Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization—0
NeRD: Neural field-based Demosaicking—0
SDAT: Sub-Dataset Alternation Training for Improved Image Demosaicing—0
MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing—0
Joint Multi-Scale Tone Mapping and Denoising for HDR Image EnhancementCode0
Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: Application to surgical imaging—0
Large-scale Global Low-rank Optimization for Computational Compressed Imaging—0
Joint Demosaicing and Deghosting of Time-Varying Exposures for Single-Shot HDR Imaging—0
A Geometric Model for Polarization Imaging on Projective Cameras—0
Deep Demosaicing for Polarimetric Filter Array CamerasCode0
Hyperspectral Demosaicing of Snapshot Camera Images Using Deep Learning—0
Enabling ISP-less Low-Power Computer Vision—0
Two-Step Color-Polarization Demosaicking Network—0
Residual Swin Transformer Channel Attention Network for Image Demosaicing—0
CRISPnet: Color Rendition ISP Net—0
PyNET-QxQ: An Efficient PyNET Variant for QxQ Bayer Pattern Demosaicing in CMOS Image SensorsCode0
Procedural Kernel Networks—0
Snapshot HDR Video Construction Using Coded Mask—0
Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot—0
Real-time division-of-focal-plane polarization imaging system with progressive networks—0
Raw Bayer Pattern Image Synthesis for Computer Vision-oriented Image Signal Processing Pipeline Design—0
Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration—0
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
High-dimensional Assisted Generative Model for Color Image RestorationCode0
Hy-demosaicing: Hyperspectral blind reconstruction from spectral subsamplingCode0
Del-Net: A Single-Stage Network for Mobile Camera ISP—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Learning to Jointly Deblur, Demosaick and Denoise Raw Images—0
Pre-demosaic Graph-based Light Field Image Compression—0
Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty—0
Projected Distribution Loss for Image EnhancementCode0
Color Image Restoration Exploiting Inter-channel Correlation with a 3-stage CNNCode0
Learning to Sample the Most Useful Training Patches from Images—0
Joint Demosaicking and Denoising Benefits from a Two-stage Training Strategy—0
Monochrome and Color Polarization Demosaicking Using Edge-Aware Residual Interpolation—0
A Review of an Old Dilemma: Demosaicking First, or Denoising First?—0
Moire Image Restoration using Multi Level Hyper Vision NetCode0
Gradient-based Feature Extraction From Raw Bayer Pattern Images—0
A Sparse Representation Based Joint Demosaicing Method for Single-Chip Polarized Color Sensor—0
Neural Network Generalization: The impact of camera parameters—0
CURL: Neural Curve Layers for Global Image EnhancementCode0
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