Large–Small-Scale Structure Blended U-Net for Brightening Low-Light Images
Numerous existing methods demonstrate impressive performance in brightening low-illumination images but fail in detail enhancement and color correction. To tackle these challenges, this paper proposes a dual-branch network including three main parts: color space transformation, a color correction ne...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/11/3382 |
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| Summary: | Numerous existing methods demonstrate impressive performance in brightening low-illumination images but fail in detail enhancement and color correction. To tackle these challenges, this paper proposes a dual-branch network including three main parts: color space transformation, a color correction network (CC-Net), and a light-boosting network (LB-Net). Specifically, we first transfer the input into the CIELAB color space to extract luminosity and color components. Afterward, we employ LB-Net to effectively explore multiscale features via a carefully designed large–small-scale structure, which can adaptively adjust the brightness of the input images. And we use CC-Net, a U-shaped network, to generate noise-free images with vivid color. Additionally, an efficient feature interaction module is introduced for the interaction of the two branches’ information. Extensive experiments on low-light image enhancement public benchmarks demonstrate that our method outperforms state-of-the-art methods in restoring the quality of low-light images. Furthermore, experiments further indicate that our method significantly enhances performance in object detection under low-light conditions. |
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| ISSN: | 1424-8220 |