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Exposure-Consistency Representation Learning for Exposure Correction

Published: 10 October 2022 Publication History

Abstract

Images captured under improper exposures including underexposure and overexposure often suffer from unsatisfactory visual effects. Since their correction procedures are quite different, it is challenging for a single network to correct various exposures. The key to addressing this issue is consistently learning underexposure and overexposure corrections. To achieve this goal, we propose an Exposure-Consistency Processing (ECP) module to consistently learn the representation of both underexposure and overexposure in the feature space. Specifically, the ECP module employs the bilateral activation mechanism that derives both underexposure and overexposure property features for exposure-consistency representation modeling, which is followed by two shared-weight branches to process these features. Based on the ECP module, we build the whole network by utilizing it as the basic unit. Additionally, to further assist the exposure-consistency learning, we develop an Exposure-Consistency Constraining (ECC) strategy that augments the various local region exposures and then constrains the feature representation change between the exposure augmented image and the original one. Our proposed network is lightweight and outperforms existing methods remarkably, while the ECP module can also be extended to other baselines, demonstrating its superiority and scalability. code: https://github.com/KevinJ-Huang/ECLNet.

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MP4 File (MM22-fp362.mp4)
10.1145/3503161.3547829

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cover image ACM Conferences
MM '22: Proceedings of the 30th ACM International Conference on Multimedia
October 2022
7537 pages
ISBN:9781450392037
DOI:10.1145/3503161
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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Published: 10 October 2022

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Author Tags

  1. consistency learning
  2. exposure correction

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  • Research-article

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  • Anhui Provincial Natural Science Foundation

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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  • (2024)Learning Exposure Correction in Dynamic ScenesProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681136(3858-3866)Online publication date: 28-Oct-2024
  • (2024)Difficulty-Aware Dynamic Network for Lightweight Exposure CorrectionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.334050634:6(5033-5048)Online publication date: Jun-2024
  • (2024)Raw Image Based Over-Exposure Correction Using Channel-Guidance StrategyIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.331176634:4(2749-2762)Online publication date: Apr-2024
  • (2024)MECNet: Multi-Scale Exposure-Consistency Learning via Fourier Transform for Exposure Correction2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831842(2440-2446)Online publication date: 6-Oct-2024
  • (2024)Real-Time Exposure Correction via Collaborative Transformations and Adaptive Sampling2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00288(2984-2994)Online publication date: 16-Jun-2024
  • (2024)A dual domain multi-exposure image fusion network based on spatial-frequency integrationNeurocomputing10.1016/j.neucom.2024.128146598(128146)Online publication date: Sep-2024
  • (2024)Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image EnhancementPattern Recognition10.1007/978-3-031-78110-0_17(260-275)Online publication date: 2-Dec-2024
  • (2024)BlazeBVD: Make Scale-Time Equalization Great Again for Blind Video DeflickeringComputer Vision – ECCV 202410.1007/978-3-031-72643-9_3(37-53)Online publication date: 22-Nov-2024
  • (2023)Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image FusionProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612561(2985-2993)Online publication date: 26-Oct-2023
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