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An Optimization Model for Infrared Image Enhancement Method Based on p-q Norm Constrained by Saliency Value

机译:基于显着性值约束的p-q范数的红外图像增强方法优化模型

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摘要

Infrared image enhancement is an important and necessary task in the infrared imaging system. In this paper, by defining the contrast in terms of the area between adjacent non-zero histogram, a novel analytical model is proposed to enlarge the areas so that the contrast can be increased. In addition, the analytical model is regularized by a penalty term based on the saliency value to enhance the salient regions as well. Thus, both of the whole images and salient regions can be enhanced, and the rank consistency can be preserved. The comparisons on 8-bit images show that the proposed method can enhance the infrared images with more details.
机译:红外图像增强是红外成像系统中重要且必要的任务。在本文中,通过根据相邻非零直方图之间的区域定义对比度,提出了一种新颖的分析模型来扩大区域,从而可以增加对比度。另外,分析模型通过基于显着性值的惩罚项进行正则化以增强显着区域。因此,可以增强整个图像和显着区域两者,并且可以保持等级一致性。通过对8位图像的比较表明,该方法可以增强红外图像的细节。

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