Full-scale feature fusion dark-light image enhancement network

Tingting Huang; Shangyou Zeng

doi:10.1117/12.3005878

10 October 2023 Full-scale feature fusion dark-light image enhancement network

Tingting Huang, Shangyou Zeng

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127995L (2023) https://doi.org/10.1117/12.3005878
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Aiming at the problems of narrow dynamic range, serious noise and color deviation in dark light images, a dark light image enhancement network based on supervised learning is proposed. The enhancement process is divided into three steps: layer decomposition, reflection recovery, and illumination adjustment. Combined with Retinex theory, three-layer Unet++ is designed to decompose the image into reflection component and illumination component. The reflection recovery firstly combines the light-guided attention mask to assign new weights to the reflection components, and then sends them to the full-scale U-shaped network for image denoising and detail restoration. Illumination adjustment adjusts the illumination adaptively. This paper is validated on several datasets with improved subjective and objective evaluation results, which can effectively suppress noise and distortion problems, and significantly improve image brightness and quality.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tingting Huang and Shangyou Zeng "Full-scale feature fusion dark-light image enhancement network", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127995L (10 October 2023); https://doi.org/10.1117/12.3005878

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KEYWORDS

Light sources and illumination

Image enhancement

Reflection

Feature fusion

Image processing

Color

Image restoration

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