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Naturalness‐Preserving Image Tone Enhancement Using Generative Adversarial Networks
Author(s) -
Son Hyeongseok,
Lee Gunhee,
Cho Sunghyun,
Lee Seungyong
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13836
Subject(s) - naturalness , computer science , tone (literature) , generative grammar , image (mathematics) , artificial intelligence , contrast (vision) , consistency (knowledge bases) , generative model , art , physics , literature , quantum mechanics
This paper proposes a deep learning‐based image tone enhancement approach that can maximally enhance the tone of an image while preserving the naturalness. Our approach does not require carefully generated ground‐truth images by human experts for training. Instead, we train a deep neural network to mimic the behavior of a previous classical filtering method that produces drastic but possibly unnatural‐looking tone enhancement results. To preserve the naturalness, we adopt the generative adversarial network (GAN) framework as a regularizer for the naturalness. To suppress artifacts caused by the generative nature of the GAN framework, we also propose an imbalanced cycle‐consistency loss. Experimental results show that our approach can effectively enhance the tone and contrast of an image while preserving the naturalness compared to previous state‐of‐the‐art approaches.