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computer-vision-1

Demosaicking

3260 papers • 126 benchmarks • 313 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

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Benchmarks

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Libraries

i

Use these libraries to find demosaicking-1 models and implementations

Datasets

PixelShift200

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Most implemented papers

Plug-and-Play Image Restoration With Deep Denoiser Prior

K. Zhang, W. Zuo, L. Gool, Radu Timofte, Yawei Li, Lei Zhang•Sun Aug 30 2020

Experimental results demonstrate that the proposed plug-and-play image restoration with deep denoiser prior not only significantly outperforms other state-of-the-art model-based methods but also achieves competitive or even superior performance against state- of theart learning- based methods.

1052

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

0

Handheld multi-frame super-resolution

P. Milanfar, Chia-Kai Liang, Bartlomiej Wronski, Ignacio Garcia-Dorado, M. Ernst, D. Kelly, Michael Krainin, M. Levoy•Tue May 07 2019

A multiframe super-resolution algorithm that creates a complete RGB image directly from a burst of CFA raw images, which is the basis of the Super-Res Zoom feature, as well as the default merge method in Night Sight mode on Google's flagship phone.

221 0

Replacing Mobile Camera ISP with a Single Deep Learning Model

L. Gool, Radu Timofte, Andrey D. Ignatov•Wed Feb 12 2020

PyNET is presented, a novel pyramidal CNN architecture designed for fine-grained image restoration that implicitly learns to perform all ISP steps such as image demosaicing, denoising, white balancing, color and contrast correction, demoireing, etc.

200 0

CURL: Neural Curve Layers for Global Image Enhancement

G. Slabaugh, S. Moran, Steven G. McDonagh•Thu Nov 28 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. Our method, dubbed neural CURve Layers (CURL), is designed as a multi-colour space neural retouching block trained jointly in three different colour spaces (HSV, CIELab, RGB) guided by a novel multi-colour space loss. The curves are fully differentiable and are trained end-to-end for different computer vision problems including photo enhancement (RGB-to-RGB) and as part of the image signal processing pipeline for image formation (RAW-to-RGB). To demonstrate the effectiveness of CURL we combine this global image transformation block with a pixel-level (local) image multi-scale encoder-decoder backbone network. In an extensive experimental evaluation we show that CURL produces state-of-the-art image quality versus recently proposed deep learning approaches in both objective and perceptual metrics, setting new state-of-the-art performance on multiple public datasets. Our code is publicly available at: https://github.com/sjmoran/CURL.

71 0

Residual Non-local Attention Networks for Image Restoration

Y. Fu, Kunpeng Li, Yulun Zhang, Kai Li, Bineng Zhong•Thu Feb 28 2019

The proposed residual local and non-local attention learning to train the very deep network is generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution.

751 0

Pyramid Attention Networks for Image Restoration

Ding Liu, Thomas S. Huang, Humphrey Shi, Y. Fu, Yuqian Zhou, Yulun Zhang, Jiahui Yu, Yuchen Fan, Yiqun Mei•Mon Apr 27 2020

A novel Pyramid Attention module for image restoration, which captures long-range feature correspondences from a multi-scale feature pyramid, and is designed to be able to borrow clean signals from their "clean" correspondences at the coarser levels.

101 0

Projected Distribution Loss for Image Enhancement

Hossein Talebi, P. Milanfar, M. Delbracio•Tue Dec 15 2020

It is demonstrated that aggregating 1D-Wasserstein distances between CNN activations is more reliable than the existing approaches, and it can significantly improve the perceptual performance of enhancement models.

40 0

A Framework for Fast Image Deconvolution With Incomplete Observations

J. Chanussot, M. Simões, L. Almeida, J. Bioucas-Dias•Tue Feb 02 2016

This paper proposes a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast, and proposes a specific implementation of this framework, based on the alternating direction method of multipliers (ADMM).

15 0

Memory-Efficient Network for Large-scale Video Compressive Sensing

Xin Yuan, Zhengjue Wang, Ziheng Cheng, Bo Chen, Guanliang Liu, Ruiying Lu•Wed Mar 03 2021

This paper develops a memory-efficient network for large-scale video SCI based on multi-group reversible 3D convolutional neural networks and takes one step further to combine demosaicing and SCI reconstruction to directly recover color video from Bayer measurements.

72 0

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Demosaicking | State-of-the-Art