Adaptive Strata Selection in Parametric Histogram Equalization for Detail-Preserving Enhancement
Abstract
A new adaptive contrast enhancement method based on Adaptive Strata Selection in Parametric Histogram Equalization (ASPHE) is proposed to adaptively select the stratification parameters according to the image content for robust contrast enhancement. Unlike the conventional SPOHE method based on the fixed stratification parameters determined empirically, the proposed ASPHE method uses the local complexity score based on the variance, entropy, and gradient information of each image region to adaptively select the number of strata. To efficiently compute the local complexity score of each image region, the integral image is employed. An adaptive mapping function is designed to guarantee the image regions with higher complexity are stratified finely while those with smoother image content are stratified coarsely. The proposed adaptive strategy has been demonstrated to enhance image contrast while avoiding the occurrence of artifacts like halos, blocking effects, and over-enhancement. The experimental results have demonstrated the ability of ASPHE to achieve an accurate approximation of the cdf while offering better adaptability to various images with efficient computation compared to the SPOHE method.
Keywords
Contrast Enhancement, Histogram Equalization, Adaptive Parameter Selection, Image Enhancement, Stratified Sampling.
Citations
IRE Journals:
Dr. V. Sesha Srinivas, J. Nikhil Raj, A. Tejagna Chandra Karthikeya, CH. K. Bhanu Pradeep, A. Blessy Bhavini Beenal "Adaptive Strata Selection in Parametric Histogram Equalization for Detail-Preserving Enhancement" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 403-416 https://doi.org/10.64388/IREV9I9-1714865
IEEE:
Dr. V. Sesha Srinivas, J. Nikhil Raj, A. Tejagna Chandra Karthikeya, CH. K. Bhanu Pradeep, A. Blessy Bhavini Beenal
"Adaptive Strata Selection in Parametric Histogram Equalization for Detail-Preserving Enhancement" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1714865
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