Abstract
Capturing images in a low-light environment are usually bothered with problems such as serious noise, color degradation, and images underexposure. Most of the low-light image enhancement approaches cannot solve the problem of the loss of the details of the result caused by noise. Inspired by the image inpainting task, we propose a novel Noise-map Guided Inpainting Network (NGI-Net) that introduces inpainting modules to restore missing information. Specifically, the algorithm is divided into two stages. Stage I decomposes input images into a reflection map, an illumination map, and a noise map inspired by the Retinex theory. These maps are passed through Stage II to fine-tune the color and details of the images based on a designed feature enhance group and a selective kernel enhance module. Experiments on real-world and synthesized datasets demonstrate the advantages and robustness of our method. The source code of our method is public in https://github.com/JaChouSSS/NGI-Net.
Z. Jiang—Student.
This work was supported in part by the National Key R&D Program of China under Grant 2017YFB0202303, in part by the National Natural Science Foundation of China under Grand 61602213 and 61772013.
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Jiang, Z., Shen, C., Li, C., Liu, H., Chen, W. (2021). Noise Map Guided Inpainting Network for Low-Light Image Enhancement. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13021. Springer, Cham. https://doi.org/10.1007/978-3-030-88010-1_17
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