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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

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Greatest papers with code

Residual Non-local Attention Networks for Image Restoration

ICLR 2019 yulunzhang/RNAN

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.

DEMOSAICKING IMAGE DENOISING SUPER-RESOLUTION

Pyramid Attention Networks for Image Restoration

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

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.

DEMOSAICKING IMAGE DENOISING SUPER-RESOLUTION

Replacing Mobile Camera ISP with a Single Deep Learning Model

13 Feb 2020aiff22/pynet

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.

DEMOSAICKING DENOISING

Handheld Multi-Frame Super-Resolution

8 May 2019kunzmi/ImageStackAlignator

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.

DEMOSAICKING MULTI-FRAME SUPER-RESOLUTION

Plug-and-Play Image Restoration with Deep Denoiser Prior

31 Aug 2020cszn/DPIR

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.

DEBLURRING DEMOSAICKING SUPER-RESOLUTION

Rethinking the Pipeline of Demosaicing, Denoising and Super-Resolution

7 May 2019guochengqian/TENet

Such a mixture problem is usually solved by a sequential solution (applying each method independently in a fixed order: DM $\to$ DN $\to$ SR), or is simply tackled by an end-to-end network without enough analysis into interactions among tasks, resulting in an undesired performance drop in the final image quality.

DEMOSAICKING DENOISING SUPER-RESOLUTION

CURL: Neural Curve Layers for Global Image Enhancement

29 Nov 2019sjmoran/CURL

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.

DEMOSAICKING DENOISING IMAGE ENHANCEMENT

Iterative Joint Image Demosaicking and Denoising using a Residual Denoising Network

16 Jul 2018cig-skoltech/deep_demosaick

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.

DEMOSAICKING DENOISING

Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks

ECCV 2018 cig-skoltech/deep_demosaick

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.

DEMOSAICKING DENOISING

Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation

18 Apr 201912dmodel/camera_sim

Unfortunately, the commonly used additive white noise (AWGN) models do not accurately reproduce the noise and the degradation encountered on these inputs.

DEMOSAICKING DENOISING IMAGE RECONSTRUCTION