Demosaicking

44 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

Most implemented papers

Plug-and-Play Image Restoration with Deep Denoiser Prior

cszn/DPIR 31 Aug 2020

Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems.

Handheld Multi-Frame Super-Resolution

kunzmi/ImageStackAlignator 8 May 2019

In this paper, we supplant the use of traditional demosaicing in single-frame and burst photography pipelines with a multiframe super-resolution algorithm that creates a complete RGB image directly from a burst of CFA raw images.

CURL: Neural Curve Layers for Global Image Enhancement

sjmoran/CURL 29 Nov 2019

We present a novel approach to adjust global image properties such as colour, saturation, and luminance using human-interpretable image enhancement curves, inspired by the Photoshop curves tool.

Residual Non-local Attention Networks for Image Restoration

yulunzhang/RNAN ICLR 2019

To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts.

Replacing Mobile Camera ISP with a Single Deep Learning Model

aiff22/pynet 13 Feb 2020

The model is trained to convert RAW Bayer data obtained directly from mobile camera sensor into photos captured with a professional high-end DSLR camera, making the solution independent of any particular mobile ISP implementation.

Pyramid Attention Networks for Image Restoration

SHI-Labs/Pyramid-Attention-Networks 28 Apr 2020

Self-similarity refers to the image prior widely used in image restoration algorithms that small but similar patterns tend to occur at different locations and scales.

Projected Distribution Loss for Image Enhancement

saurabh-kataria/projected-distribution-loss 16 Dec 2020

More explicitly, we show that in imaging applications such as denoising, super-resolution, demosaicing, deblurring and JPEG artifact removal, the proposed learning loss outperforms the current state-of-the-art on reference-based perceptual losses.

Memory-Efficient Network for Large-scale Video Compressive Sensing

BoChenGroup/RevSCI-net CVPR 2021

With the knowledge of masks, optimization algorithms or deep learning methods are employed to reconstruct the desired high-speed video frames from this snapshot measurement.

A Framework for Fast Image Deconvolution with Incomplete Observations

alfaiate/DeconvolutionIncompleteObs 3 Feb 2016

In this paper, we propose a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast.

Dirty Pixels: Towards End-to-End Image Processing and Perception

princeton-computational-imaging/DirtyPixels 23 Jan 2017

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.