Hazy Image Decolorization with Color Contrast Restoration.

IF 10.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wei Wang, Zhengguo Li, Shiqian Wu, Liangcai Zeng
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引用次数: 0

Abstract

It is challenging to convert a hazy color image into a gray-scale image because the color contrast field of a hazy image is distorted. In this paper, a novel decolorization algorithm is proposed to transfer a hazy image into a distortionrecovered gray-scale image. To recover the color contrast field, the relationship between the restored color contrast and its distorted input is presented in CIELab color space. Based on this restoration, a nonlinear optimization problem is formulated to construct the resultant gray-scale image. A new differentiable approximation solution is introduced to solve this problem with an extension of the Huber loss function. Experimental results show that the proposed algorithm effectively preserves the global luminance consistency while represents the original color contrast in gray-scales, which is very close to the corresponding ground truth gray-scale one.

利用色彩对比度修复技术为模糊图像脱色
将模糊彩色图像转换成灰度图像是一项挑战,因为模糊图像的色彩对比场会失真。本文提出了一种新颖的脱色算法,可将模糊图像转换为失真恢复后的灰度图像。为了恢复色彩对比度场,在 CIELab 色彩空间中提出了恢复的色彩对比度与其失真输入之间的关系。在此基础上,提出了一个非线性优化问题,以构建灰度图像的结果。为了解决这个问题,引入了一个新的可微分近似解决方案,并对 Huber 损失函数进行了扩展。实验结果表明,所提出的算法有效地保持了全局亮度的一致性,同时在灰度上体现了原始的色彩对比度,非常接近相应的地面真实灰度图像。
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来源期刊
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing 工程技术-工程:电子与电气
CiteScore
20.90
自引率
6.60%
发文量
774
审稿时长
7.6 months
期刊介绍: The IEEE Transactions on Image Processing delves into groundbreaking theories, algorithms, and structures concerning the generation, acquisition, manipulation, transmission, scrutiny, and presentation of images, video, and multidimensional signals across diverse applications. Topics span mathematical, statistical, and perceptual aspects, encompassing modeling, representation, formation, coding, filtering, enhancement, restoration, rendering, halftoning, search, and analysis of images, video, and multidimensional signals. Pertinent applications range from image and video communications to electronic imaging, biomedical imaging, image and video systems, and remote sensing.
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