Dual-Plateau Histogram Equalization Algorithm Optimized for Infrared Images with Low-Contrast Small Targets

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This paper proposes a dual-plateau histogram equalization algorithm specifically designed to enhance infrared images, particularly for improving the visibility of low-contrast small targets. The implementation involves preprocessing, dual-platform histogram equalization, and weighted averaging to boost image contrast.

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In this paper, we address the challenge of detecting low-contrast small targets in infrared images by proposing a dual-plateau histogram equalization algorithm. This method effectively enhances infrared image contrast, making faint small targets more distinguishable. The implementation involves three key steps: First, infrared images undergo preprocessing to normalize intensity values. Second, the preprocessed image is projected onto two separate plateau levels for independent histogram equalization, where plateau thresholds control contrast stretching ranges to prevent over-enhancement of background noise. Finally, a weighted averaging operation merges the two equalized images using optimized coefficients to maximize target prominence. Experimental results demonstrate that our dual-plateau approach significantly improves infrared image quality and outperforms conventional methods in target detection tasks by balancing contrast enhancement and noise suppression.

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