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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 76–100 of 150 papers

TitleStatusHype
Learning to Jointly Deblur, Demosaick and Denoise Raw Images—0
PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal ProcessingCode1
Memory-Efficient Network for Large-scale Video Compressive SensingCode1
Pre-demosaic Graph-based Light Field Image Compression—0
Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst ImagesCode1
Plug-and-Play Algorithms for Video Snapshot Compressive ImagingCode1
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
AIM 2020 Challenge on Learned Image Signal Processing PipelineCode1
Joint Demosaicking and Denoising Benefits from a Two-stage Training Strategy—0
Plug-and-Play Image Restoration with Deep Denoiser PriorCode1
Monochrome and Color Polarization Demosaicking Using Edge-Aware Residual Interpolation—0
An Adaptive Neural Network for Unsupervised Mosaic Consistency Analysis in Image ForensicsCode1
Joint Demosaicing and Denoising With Self GuidanceCode1
Pyramid Attention Networks for Image RestorationCode1
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
Replacing Mobile Camera ISP with a Single Deep Learning ModelCode1
A Sparse Representation Based Joint Demosaicing Method for Single-Chip Polarized Color Sensor—0
Neural Network Generalization: The impact of camera parameters—0
Fully Trainable and Interpretable Non-Local Sparse Models for Image RestorationCode1
CURL: Neural Curve Layers for Global Image EnhancementCode0
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