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Jing Xue

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SPIE Journal Paper | 8 March 2023

Enhancement estimation network for flexibly enhancing low-light images via lighting level estimation

Zhongshuo Zhuo, Yanwen Xing, Luqing Luo, Jing Xue, Yun Wang, Jian Liu

JEI, Vol. 32, Issue 02, 023005, (March 2023) https://doi.org/10.1117/12.10.1117/1.JEI.32.2.023005

KEYWORDS: Image enhancement, Light sources and illumination, Facial recognition systems, Education and training, Image fusion, Control systems, Network architectures, Image filtering, RGB color model, Image quality

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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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