Advanced deep learning enhancement algorithm based on retinex model guidance

Hangying Zhang; Liangcai Cao

doi:10.1117/12.2619492

8 July 2022 Advanced deep learning enhancement algorithm based on retinex model guidance

Hangying Zhang, Liangcai Cao

Proceedings Volume 12281, 2021 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology; 122810G (2022) https://doi.org/10.1117/12.2619492
Event: 2021 International Conference on Optical Instruments and Technology, 2022, Online Only

Abstract

Traditional Retinex model-based image enhancement methods require careful design of constraints and parameters to handle this highly ill-conditioned decomposition. With the advancement of deep learning algorithms, low-light image enhancement has also achieved deep processing. However, image enhancement based on the RGB color space model is prone to color distortions when enhancing images under the influence of the correlation of the three primary colors of RGB. In this report we apply the HSV color space technique to a Retinex-based network model. Simulations and experiments show that using HSV space-improved deep neural networks can effectively avoid the color distortion problem.

Citation Download Citation

Hangying Zhang and Liangcai Cao "Advanced deep learning enhancement algorithm based on retinex model guidance", Proc. SPIE 12281, 2021 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 122810G (8 July 2022); https://doi.org/10.1117/12.2619492

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KEYWORDS

Image enhancement

RGB color model

Image fusion

Image restoration

Algorithm development

Image processing

Visualization

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