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Xin Du

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Proceedings Article | 30 April 2024 Paper

Research on camouflaged object image enhancement using subblock histogram equalization in HSV space

Hongbin Xu, Jianjun Sha, Xin Du, Laishuo Jing

Proceedings Volume 13157, 131570C (2024) https://doi.org/10.1117/12.3015094

KEYWORDS: Histograms, Image enhancement, RGB color model, Image processing, Image quality, Camouflage, Detection and tracking algorithms, Color, Animals, Soldiers

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Animals in nature always incorporate their own color, texture, shape, and other features into the surrounding environment to achieve camouflage while trying to hunt and avoid natural enemies. Humans have also successfully applied this concept to various fields such as military camouflage, wilderness exploration, and endangered animal protection by imitating animal behaviors. However, these behaviors also make it difficult to identify camouflaged objects in images. To address this issue, a camouflage image enhancement algorithm based on HSV and histogram equalization is proposed in this paper. First, convert the camouflage image from RGB to HSV space and afterward process only the V component. Next, calculate the peaks and valleys of the V-component histogram, whereby the histogram is partitioned into several peak regions. Then, histogram equalization and dynamic range regression are performed separately for each region of the image corresponding to the histogram peak. Finally, the processed regions are combined to generate the enhanced image. The proposed algorithm is evaluated using the CAMO dataset, and the results show that our method outperforms similar algorithms in terms of image quality measures such as PIQE, ILNIQE, OG-IQA, and BLIINDS-II, in both the overall image and the camouflaged region. In contrast, for the non-camouflaged region, our method is mostly weaker than other methods in all measures, indicating that our method has a positive enhancement for the camouflaged object and less influence on the background region.

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