Demosaicking
54 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
Dirty Pixels: Towards End-to-End Image Processing and Perception
princeton-computational-imaging/DirtyPixels
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As such, conventional imaging involves processing the RAW sensor measurements in a sequential pipeline of steps, such as demosaicking, denoising, deblurring, tone-mapping and compression.
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems
tum-vision/learn_prox_ops
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ICCV 2017
While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks.
Reconfiguring the Imaging Pipeline for Computer Vision
We propose a new image sensor design that can compensate for skipping these stages.
Deep Mean-Shift Priors for Image Restoration
siavashbigdeli/DMSP
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NeurIPS 2017
We show that the gradient of our prior corresponds to the mean-shift vector on the natural image distribution.
Consensus Convolutional Sparse Coding
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision.
Boundary-based Image Forgery Detection by Fast Shallow CNN
In this paper, we substantiate that Fast SCNN can detect drastic change of chroma and saturation.
Deep Residual Network for Joint Demosaicing and Super-Resolution
By training on high-quality samples, our deep residual demosaicing and super-resolution network is able to recover high-quality super-resolved images from low-resolution Bayer mosaics in a single step without producing the artifacts common to such processing when the two operations are done separately.
Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks
cig-skoltech/deep_demosaick
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ECCV 2018
Demosaicking and denoising are among the most crucial steps of modern digital camera pipelines and their joint treatment is a highly ill-posed inverse problem where at-least two-thirds of the information are missing and the rest are corrupted by noise.
Low Cost Edge Sensing for High Quality Demosaicking
Compared to methods of similar computational cost, our method achieves substantially higher accuracy, Whereas compared to methods of similar accuracy, our method has significantly lower cost.
Iterative Joint Image Demosaicking and Denoising using a Residual Denoising Network
cig-skoltech/deep_demosaick
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Modern approaches try to jointly solve these problems, i. e. joint denoising-demosaicking which is an inherently ill-posed problem given that two-thirds of the intensity information is missing and the rest are perturbed by noise.
