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Preventing Over-Enhancement Using Modified ICSO Algorithm
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AI
DOI：10.1109/ACCESS.2023.3279254.png)
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摘要
En 中文
This paper proposes an Image Contrast Enhancement (ICE) method based on using an Improved Chicken Swarm Optimization (ICSO) algorithm to enhance images while at the same time preventing over-enhancement. In the optimization process, a new practical objective function is employed to reach three main goals, preserving the main details, generating an image with a uniform histogram, and reducing the spikes in the modified histogram. In the proposed approach, the RGB color channels are optimized individually. The performance of the proposed method is suitable for enhancing the contrast of low- and high-contrast images. A subjective experiment is designed to visually evaluate and compare the results with other ICE methods. The simulation results on the CSIQ, TID2013, and SEID datasets show that the proposed method outperforms numerous traditional and state-of-the-art ICE techniques both subjectively and objectively. The most important advantage of the newly proposed technique is that there is an agreement among observers on when over-enhancement occurs regardless of whether the Initial processed image was of low or high contrast.
Keyword:
Histograms
Optimization
Brightness
Image enhancement
Image color analysis
Classification algorithms
Particle swarm optimization
Contrast enhancement
image quality assessment
over-enhancement
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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