Global double gamma correction with improved SSA for low-light image enhancement
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School of Electrical Engineering, Xinjiang University, Urumqi 830049, China

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TP391

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    Abstract:

    To address the problems of low contrast, edge detail loss and excessive enhancement in existing low-light image enhancement algorithms, a low-light image enhancement method based on the combination of global double gamma correction and improved SSA algorithm is proposed. In addition, to improve the convergence performance of the algorithm, elite backward learning and Lévy flight strategy are introduced to improve the sparrow algorithm, optimize the selection of parameter (α), and realize the detail enhancement of the image by finding the optimal gamma value. The simulation experimental results show that the algorithm enhances the image with larger peak signal-to-noise ratio and structural similarity index, less image color distortion, and sharpens the edges, and the overall enhancement effect is better than other comparison algorithms, which has better processing effect.

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  • Received:
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  • Online: February 05,2024
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