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Feature Based Deep Retinex for Low-Light Image Enhancement

Published: 29 May 2021 Publication History

Abstract

Low-light image processing is a common issue in industry, media and other practical application fields. Enhancing image brightness or contrast directly may bring accompanying noise and color cast. The proposed method is a feature based deep F-Retinex-Net. A neural network is used to decompose the image into features representation of reflectance component and illumination component. The feature of illumination component was enhanced, and then the features are used to reconstruct the enhanced image by another network. The convergence of the observation model is confirmed by observing the state of illumination and reflectance features by visualization method. Experimental results show that F-Retinex-Net has better performance enhancement effect than Retinex-Net, and can also greatly alleviate the color distortion problem.

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  1. Feature Based Deep Retinex for Low-Light Image Enhancement
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        cover image ACM Other conferences
        ICAIP '20: Proceedings of the 4th International Conference on Advances in Image Processing
        November 2020
        191 pages
        ISBN:9781450388368
        DOI:10.1145/3441250
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        New York, NY, United States

        Publication History

        Published: 29 May 2021

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        Author Tags

        1. Retinex
        2. deep learning
        3. feature
        4. low-light image enhancement

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