亮度
计算机科学
计算机视觉
人工智能
图像增强
面子(社会学概念)
图像复原
对比度(视觉)
相似性(几何)
图像处理
模式识别(心理学)
图像(数学)
光学
物理
社会科学
社会学
作者
Zhongshuo Zhuo,Yanwen Xing,Luqing Luo,Jian Xue,Yun Wang,Jian Liu
标识
DOI:10.1117/1.jei.32.2.023005
摘要
Although existing methods have achieved impressive accomplishments in the field of low-light image enhancement, the phenomenon of overenhancement remains a challenge. To address this issue, a multiport output enhancement structure combined with multiloss functions supervision is designed to obtain multiple images with different enhancement intensity. Subsequently, an enhancement estimation module is proposed to flexibly select the most suitable enhancement image from these series of enhanced images, thereby reducing overexposure. To this end, the enhancement estimation network (EENet) for enhancing low-light images is introduced. The proposed EENet enhances low-illumination images with different brightness levels more perfectly because the phenomenon of overenhancement is reduced. For qualitative comparison, the experimental results demonstrate that the proposed method enhances low-illumination images more naturally, especially for enhancing low-illumination images that have brighter illumination. For quantitative evaluation, the proposed method obtains the highest peak signal-to-noise ratio and structural similarity on the public single image contrast enhancer dataset and reconstructed low-light* dataset compared with those listed methods. In addition, the EENet was proven to outperform state-of-the-art methods in dark image face detection, indicating that the EENet has great practical potential.
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