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用于低曝光图像增强的图像细分与四重裁剪自适应直方图均衡化(ISQCAHE)

Image sub-division and quadruple clipped adaptive histogram equalization (ISQCAHE) for low exposure image enhancement.

作者信息

Acharya Upendra Kumar, Kumar Sandeep

机构信息

Department of Electronics and Communication Engineering, National Institute of Technology, Delhi, India.

Department of Electronics and Communication Engineering, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh India.

出版信息

Multidimens Syst Signal Process. 2023;34(1):25-45. doi: 10.1007/s11045-022-00853-9. Epub 2022 Sep 28.

Abstract

In this paper, a novel image sub-division and quadruple clipped adaptive histogram equalization (ISQCAHE) technique is proposed for the enhancement of low exposure images. The proposed method involves, computation of the histogram which includes a new approach of image sub-division, enhancement controlling mechanism, modification of probability density function (PDF) and histogram equalization (HE). The original histogram is segmented into sub-histograms based on exposure threshold and mean, to preserve the brightness and entropy. Then, individual sub-histogram is clipped separately to control the enhancement rate. For enhancing the visual quality, HE is applied to individual sub-histogram using the modified PDF. The experimental results show that, the proposed ISQCAHE method avoids the unpleasant artifacts effectively and provide a natural appearance to the enhanced image. It is simple, adaptive and performs superior than other techniques in terms of visual quality, absolute mean brightness error, entropy, Natural image quality evaluation, brightness preservation, structure similarity index measure and feature similarity index measure.

摘要

本文提出了一种用于增强低曝光图像的新型图像细分与四重裁剪自适应直方图均衡化(ISQCAHE)技术。该方法包括直方图计算,其中涉及图像细分的新方法、增强控制机制、概率密度函数(PDF)的修改以及直方图均衡化(HE)。基于曝光阈值和均值将原始直方图分割为子直方图,以保留亮度和熵。然后,分别对各个子直方图进行裁剪以控制增强率。为了提高视觉质量,使用修改后的PDF对各个子直方图应用HE。实验结果表明,所提出的ISQCAHE方法有效地避免了令人不悦的伪影,并为增强后的图像提供了自然的外观。它简单、自适应,并且在视觉质量、绝对平均亮度误差、熵、自然图像质量评估、亮度保留、结构相似性指数测量和特征相似性指数测量方面比其他技术表现更优。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd4a/9518955/a4c8dcd4bdac/11045_2022_853_Fig1_HTML.jpg

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