基于自动模糊识别和总变差最小化的图像盲恢复

XiuQin Su, Xiang Li, Lei Ji
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引用次数: 3

摘要

本文提出了一种自适应盲图像恢复算法。针对某些模糊可能导致倒谱域中特定分量失真的特点,提出了一种利用Fourier-Mellin变换对倒谱点扩散函数(PSF)进行自动分类识别的算法:对线性运动模糊、散焦模糊等常见类型的模糊,采用维纳滤波对其进行恢复;而对于其他问题,我们提出了一种改进的全变差(TV)盲恢复算法。该算法将典型图像复原方法与盲复原方法相结合,实现了自适应盲复原图像。实验结果表明,该方法在不同环境下都能有效地恢复退化图像,并显著提高了高噪声条件下的恢复性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blind image restoration based on automatic blur identify and total variation minimization
In this paper, an adaptive blind image restoration algorithm is proposed. According to the feature that the certain blur may lead to the specific component distortion in the cepstral domain, we develop an automatic algorithm using Fourier-Mellin transform to classify and identify the point spread function (PSF) with cepstrum: for the usual types of blur, such as linear motion blur and defocus blur, we restore it with Wiener filter; while for others, we propose an improved total variation (TV) blind restoration algorithm. The algorithm combines the typical methods and blind methods of image restoration, achieving the adaptive blind image restoration. Experimental results show that it is effective in restoring degraded images under different environments, and it improves the restoring performance significantly under the presence of high noise level.
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