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A convolutional neural network based method for low-illumination image enhancement

Authors:

Haifeng WangAuthors Info & Claims

AIPR '19: Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition

Pages 72 - 77

https://doi.org/10.1145/3357254.3357255

Published: 16 August 2019 Publication History

Abstract

Nowadays, images can be conveniently captured by various image acquisition devices. Weak lighting conditions and devices with poor filling flash will produce low-illumination images. These degraded images are difficult to identify, and must be processed by some methods through the computer. With the inspiring performance of convolutional neural network (CNN) in image classification, object detection and tracking, some studies have been made to enhance low-illumination images by using CNN in recent years. In this paper, based on the existing researches of CNN based low-illumination image enhancement, an improved Unet model is proposed to enhance low-illumination images. At the same time, this paper introduces two new loss functions: Peak signal-to-noise ratio (PSNR) loss and multi-scale Structural similarity (MS-SSIM) loss, and use a mixture of these two loss functions as loss function in our model. Our method can effectively balance the brightness of the processed image, accurately restore the color, so that the enhanced image have a better perception. Results demonstrate that the proposed method outperforms other enhancement methods.

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Dai JShi NZhang TXu W(2024)TCME: Thin Cloud Removal Network for Optical Remote Sensing Images Based on Multidimensional Features EnhancementIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.346486262(1-16)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3464862
Hussain SChalicham NGarine LChunduru SNikitha VPrasad V PSanki P(2024)Low-Light Image Restoration Using a Convolutional Neural NetworkJournal of Electronic Materials10.1007/s11664-024-11079-953:7(3582-3593)Online publication date: 8-May-2024
https://doi.org/10.1007/s11664-024-11079-9
Guo YGu MXu Z(2023)Research on the Improvement of Semi-Global Matching Algorithm for Binocular Vision Based on Lunar Surface EnvironmentSensors10.3390/s2315690123:15(6901)Online publication date: 3-Aug-2023
https://doi.org/10.3390/s23156901
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Index Terms

A convolutional neural network based method for low-illumination image enhancement
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Published In

cover image ACM Other conferences

AIPR '19: Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition

August 2019

198 pages

ISBN:9781450372299

DOI:10.1145/3357254

Conference Chairs:
Li Ma
North China University of Technology, China
,
Xu Huang
University of Canberra, Australia

Copyright © 2019 ACM.

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 August 2019

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AIPR 2019

AIPR 2019: 2019 2nd International Conference on Artificial Intelligence and Pattern Recognition

August 16 - 18, 2019

Beijing, China

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Cited By

Dai JShi NZhang TXu W(2024)TCME: Thin Cloud Removal Network for Optical Remote Sensing Images Based on Multidimensional Features EnhancementIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.346486262(1-16)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3464862
Hussain SChalicham NGarine LChunduru SNikitha VPrasad V PSanki P(2024)Low-Light Image Restoration Using a Convolutional Neural NetworkJournal of Electronic Materials10.1007/s11664-024-11079-953:7(3582-3593)Online publication date: 8-May-2024
https://doi.org/10.1007/s11664-024-11079-9
Guo YGu MXu Z(2023)Research on the Improvement of Semi-Global Matching Algorithm for Binocular Vision Based on Lunar Surface EnvironmentSensors10.3390/s2315690123:15(6901)Online publication date: 3-Aug-2023
https://doi.org/10.3390/s23156901
Kwon HLee S(2023)Raindrop-Removal Image Translation Using Target-Mask Network with Attention ModuleMathematics10.3390/math1115331811:15(3318)Online publication date: 28-Jul-2023
https://doi.org/10.3390/math11153318
Dash SParida PMohanty J(2023)Illumination robust deep convolutional neural network for medical image classificationSoft Computing10.1007/s00500-023-07918-2Online publication date: 14-Feb-2023
https://doi.org/10.1007/s00500-023-07918-2
Tang HZhu HTao HXie C(2022)An Improved Algorithm for Low-Light Image Enhancement Based on RetinexNetApplied Sciences10.3390/app1214726812:14(7268)Online publication date: 19-Jul-2022
https://doi.org/10.3390/app12147268
Sameena Sreenivasulu E(2022)Low-light Color Image Enhancement based on Dark Channel Prior with Retinex Model2022 Smart Technologies, Communication and Robotics (STCR)10.1109/STCR55312.2022.10009364(1-6)Online publication date: 10-Dec-2022
https://doi.org/10.1109/STCR55312.2022.10009364
Yadav OGhosal KLutz SSmolic A(2021)Frequency-domain loss function for deep exposure correction of dark imagesSignal, Image and Video Processing10.1007/s11760-021-01915-4Online publication date: 15-May-2021
https://doi.org/10.1007/s11760-021-01915-4
Yadav GYadav D(2021)Multiple feature-based contrast enhancement of ROI of backlit imagesMachine Vision and Applications10.1007/s00138-021-01272-933:1Online publication date: 27-Dec-2021
https://doi.org/10.1007/s00138-021-01272-9
Yadav GYadav DMouli P(2021)Enhancement of Region of Interest from a Single Backlit Image with Multiple FeaturesComputer Vision and Image Processing10.1007/978-981-16-1092-9_39(467-476)Online publication date: 28-Mar-2021
https://doi.org/10.1007/978-981-16-1092-9_39

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