Low-light image enhancement based on Multi-illumination estimation and Multi-scale fusion
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Abstract:
To improve image quality under low illumination conditions, a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion (MIMS). Firstly, the illumination is processed by contrast-limited adaptive histogram equalization (CLAHE), adaptive complementary gamma function (ACG), and adap-tive detail preserving S-curve (ADPS), respectively, to obtain three components. Then, the fusion-relevant features, exposure, and color contrast are selected as the weight maps. Subsequently, these components and weight maps are fused through multi-scale to generate enhanced illumination. Finally, the enhanced images are obtained by multiplying the en-hanced illumination and reflectance. Compared with existing approaches, this proposed method achieves an average in-crease of 0.81% and 2.89% in the SSIM and PSNR, and a decrease of 6.17% and 32.61% in the NIQE and GMSD, re-spectively.
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Project Supported:
The National Key R&D Program of China (2022YFB3205101)
Clc Number:
