Enhancement method of low illumination image based on deep neural network integration

Jun Liu; Shuai Wang; Shaohui Qu

doi:10.1117/12.3033723

13 June 2024 Enhancement method of low illumination image based on deep neural network integration

Jun Liu, Shuai Wang, Shaohui Qu

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318008 (2024) https://doi.org/10.1117/12.3033723
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Aiming at the problem that existing low-illumination image enhancement methods cannot simultaneously take into account multiple degrading factors, which leads to poor processing effect when applied to real scenes, deep neural network models facing different image processing tasks are integrated based on neural network integration strategy, and multi-task and multi-stage cascade processing is performed. The low illumination image can be enhanced under the interference of multiple degrading factors. The experimental results show that the proposed method can significantly improve the overall brightness and contrast of the image, and the enhanced image has natural colour distribution and good visual effect, which is obviously superior to the existing mainstream low-illumination image enhancement methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jun Liu, Shuai Wang, and Shaohui Qu "Enhancement method of low illumination image based on deep neural network integration", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318008 (13 June 2024); https://doi.org/10.1117/12.3033723

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KEYWORDS

Image enhancement

Image processing

Neural networks

Image quality

Light sources and illumination

Education and training

Deep learning

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