Adaptive Image Enhancement based on Bi-Histogram Equalization with a clipping limit

• A new variant of histogram equalization technique is proposed.
• Histogram segmentation allows mean brightness preservation.
• Flexibility in selecting clipping limit during histogram clipping enables better restriction on enhancement rate.
• Proposed technique demonstrates excellent performance in entropy, PSNR and AMBE measurements.

A new approach based on Bi-Histogram Equalization is presented to enhance grayscale images. The proposed Adaptive Image Enhancement based on Bi-Histogram Equalization (AIEBHE) technique divides the input histogram into two sub-histograms, which are at the threshold of the histogram median for mean brightness preservation. Histogram clipping is performed to control the enhancement rate, and then the clipped sub-histograms are equalized and integrated to obtain the enhanced image. The novelty of AIEBHE is its flexibility in choosing the clipping limit that automatically selects the smallest value among histogram bins, mean, and median values, resulting in the conservation of a greater amount of information in the image. Automatic selection of the clipping limit addresses the issue of over-emphasizing of high frequency bins during histogram equalization. Simulation results reveal that AIEBHE technique outperforms other histogram-equalization-based enhancement techniques in terms of detail preservation and mean brightness preservation.

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