Multi-scale low-light image enhancement by fusing dense residual blocks

Ting Sun; Lingling Fu; Yuanbo Tan

doi:10.1117/12.2639474

27 June 2022 Multi-scale low-light image enhancement by fusing dense residual blocks

Ting Sun, Lingling Fu, Yuanbo Tan

Proceedings Volume 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022); 1225318 (2022) https://doi.org/10.1117/12.2639474
Event: Second International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 2022, Qingdao, China

Abstract

The existing low-light image enhancement algorithms could not process brightness, contrast, color, and other details at the same time. Thus, a multi-scale low-light image enhancement network which fused with dense residual blocks is proposed. The image is first generated with feature-rich input through an input module, then the features are fed into a multi-scale backbone enhancement network with dense residual blocks, and finally a refinement module is used to enrich image details and remove halos. The experimental results show that the proposed method better improves the contrast and brightness, actualizes the color, enriches the texture details, and decreases the noise and artifacts in low-light images, which quantitatively and qualitatively demonstrated its advantages comparing with other mainstream methods.

Citation Download Citation

Ting Sun, Lingling Fu, and Yuanbo Tan "Multi-scale low-light image enhancement by fusing dense residual blocks", Proc. SPIE 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 1225318 (27 June 2022); https://doi.org/10.1117/12.2639474

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KEYWORDS

Image enhancement

Image quality

Visualization

Machine learning

Convolution

Algorithm development

Image processing

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