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Blind image restoration based on automatic blur identify and total variation minimization

机译:基于自动模糊识别和最小化总偏差的盲图恢复

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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.
机译:本文提出了一种自适应盲图像复原算法。根据某些模糊可能会导致倒频谱域中特定分量失真的特征,我们开发了一种使用傅里叶-梅林变换的自动算法,以对倒频谱进行点扩散函数(PSF)的分类和识别:对于常见的模糊类型,例如线性运动模糊和散焦模糊,我们使用维纳滤镜对其进行还原;而对于其他人,我们提出了一种改进的总变异(TV)盲恢复算法。该算法结合了典型的图像复原方法和盲目复原方法,实现了自适应的盲目复原。实验结果表明,该方法可以有效地恢复不同环境下的退化图像,并且在存在高噪声水平的情况下可以显着提高恢复性能。

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