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Adaptive gamma correction based on cumulative histogram for enhancing near-infrared images

机译:基于累积直方图的自适应伽玛校正,可增强近红外图像

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摘要

Histogram-based methods have been proven their ability in image enhancement. To improve low contrast while preserving details and high brightness in near-infrared images, a novel method called adaptive gamma correction based on cumulative histogram (AGCCH) is studied in this paper. This novel image enhancement method improves the contrast of local pixels through adaptive gamma correction (AGC), which is formed by incorporating a cumulative histogram or cumulative sub-histogram into the weighting distribution. Both qualitatively and quantitatively, experimental results demonstrate that the proposed image enhancement with the AGCCH method can perform well in brightness preservation, contrast enhancement, and detail preservation, and it is superior to previous state-of-the-art methods. (C) 2016 Elsevier B.V. All rights reserved.
机译:基于直方图的方法已被证明具有增强图像的能力。为了在保留近红外图像细节和高亮度的同时改善低对比度,本文研究了一种基于累积直方图的自适应伽马校正(AGCCH)新方法。这种新颖的图像增强方法通过自适应伽马校正(AGC)改善了局部像素的对比度,该方法是通过将累积直方图或累积子直方图合并到加权分布中而形成的。从定性和定量两个方面,实验结果均表明,采用AGCCH方法提出的图像增强功能在亮度保持,对比度增强和细节保持方面表现良好,并且优于以前的最新方法。 (C)2016 Elsevier B.V.保留所有权利。

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