Tripartite sub-image histogram equalization for slightly low contrast gray-tone image enhancement. (February 2023)

• Record Type: Journal Article
• Authors: Rahman, Hafijur; Paul, Gour Chandra
• Is Part Of: Pattern recognition. Volume 134(2023)
• Publication Date: 2023-02
• Subjects: Contrast enhancement -- Slightly low contrast image -- Histogram equalization -- Mean brightness sustainability -- Image quality assessment -- Bit-plane specific measure; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.109043
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A robust TSIHE method is proposed for slightly low contrast image enhancement, a less explored field in the literature. The proposed method is tested using 60 standard slightly low contrast test images. The proposed method enhances the image contrast and preserves the mean brightness very well. Statistical analyses reveal improvement in the BRISQUE, GMSD, MSSIM, FSIM, and PSNR measurements. Abstract: In this paper, a neoteric tripartite sub-image histogram equalization method is proposed to enhance slightly low contrast gray-tone images, which is a less explored area in the literature. An image is decomposed into three sub-images to preserve its mean brightness, and the histograms of the sub-images are calculated. Then, the snipping procedure is applied to each histogram to constrain the pace of contrast enhancement. Subsequently, the equalization of the three histograms is performed independently, and finally, the three equalized sub-images are composed into a single image. The proposed method offers better outcomes as compared to several common and state-of-the-art histogram equalization-based methods regarding contrast improvement, blind/reference-less image spatial quality evaluator, mean brightness preservation, peak signal-to-noise ratio, mean structural similarity, gradient magnitude similarity deviation, feature similarity, bit-plane to bit-plane similarity, and visual image quality.