Contrast Limited Adaptive Local Histogram Equalization Method for Poor Contrast Image Enhancement

This study presents a novel approach for Contrast Limited Adaptive Local Histogram Equalization Method for Poor Contrast Image Enhancement.Contrast Limited Adaptive Histogram Equalization (CLAHE) is widely used for image enhancement due to its simplicity and speed, but it suffers from manual parameter selection and fixed weighting, which can cause artifacts. To overcome these limitations, this paper proposes a modified approach, Contrast Limited Adaptive Local Histogram Equalization (CLALHE), which enhances image contrast locally and adaptively without user input. CLALHE applies multiple preliminary enhancements to determine optimal parameters, then divides the image into subimages and applies these parameters individually to emphasize local features. The enhanced subimages are then combined to form the final image. Experiments were conducted on three datasets: DIARETDB1, Pasadena Houses 2000. Qualitative results show improved contrast and clearer details with minimal artifacts. Quantitative evaluation demonstrates superior performance in PSNR, entropy, AMBE, SSIM, CII, and RMSE. CLALHE effectively balances enhancement quality and computational efficiency. The method performs robustly across different image types.

Keywords: Image processing, contrast enhancement, histogram equalization, CLAHE, sub-image