一种新的彩色图像正则化盲图像反卷积方案

Yu He, Kim-Hui Yap, Li Chen, Lap-Pui Chau
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引用次数: 5

摘要

提出了一种新的正则化方案来解决彩色图像的盲反卷积问题。传统的盲单色图像反卷积算法独立处理每个颜色通道,从而忽略了彩色图像中存在的通道间相关性。此外,现有的大多数盲颜色反卷积算法都没有考虑到模糊的参数信息。针对这些问题,提出了一种用于彩色图像盲反卷积的正则化方案。提出了一种新的模糊域正则化算子。采用强化模糊建模方案评估流形参数模糊结构的相关性,并将信息集成到反卷积方案中。此外,还提出了一种图像正则化方案,以恢复彩色图像的边缘,减少彩色伪影。实验结果表明,该方法能够在噪声环境下获得满意的彩色图像恢复效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new color image regularization scheme for blind image deconvolution
This paper proposes a new regularization scheme to address blind color image deconvolution. Conventional blind monochromatic image deconvolution algorithms handle each color channel independently, thereby ignoring the inter-channel correlation present in the color images. Further, most existing blind color deconvolution algorithms do not take the parametric information of the blurs into consideration. In view of these, a regularization scheme is proposed to perform blind color image deconvolution. A new regularization operator is developed in the blur domain. A reinforcement blur modeling scheme is adopted to evaluate the relevance of manifold parametric blur structures, and the information is integrated into the deconvolution scheme. In addition, a regularization scheme for image is developed to recover edges of color images and reduce color artifacts. Experimental results show that the method is able to achieve satisfactory restored color images under noisy environment.
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