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Independent component analysis and nongaussianity for blind image deconvolution and deblurring

机译：独立分量分析和非高斯性用于盲图像去卷积和去模糊

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摘要

Blind deconvolution or deblurring is a challenging problem in many signal processing applications as signals and images often suffer from blurring or point spreading with unknown blurring kernels or point-spread functions as well as noise corruption. Most existing methods require certain knowledge about both the signal and the kernel and their performance depends on the amount of prior information regarding the both. Independent component analysis (ICA) has emerged as a useful method for recovering signals from their mixtures. However, ICA usually requires a number of different input signals to uncover the mixing mechanism. In this paper a blind deconvolution and deblurring method is proposed based on the nongaussianity measure of ICA as well as a genetic algorithm. The method is simple and does not require prior knowledge regarding either the image or the blurring process, but is able to estimate or approximate the blurring kernel from a single blurred image. Various blurring functions are described and discussed. The proposed method has been tested on images degraded by different blurring kernels and the results are compared to those of existing methods such as Wiener filter, regularization filter, and the Richardson-Lucy method. Experimental results show that the proposed method outperform these methods.
机译：在许多信号处理应用中，盲去卷积或去模糊是一个具有挑战性的问题，因为信号和图像经常会因未知的模糊内核或点扩展功能而遭受模糊或点扩展，以及噪声破坏。大多数现有方法都需要有关信号和内核的某些知识，并且它们的性能取决于有关二者的先验信息量。独立成分分析（ICA）已成为一种从混合物中恢复信号的有用方法。但是，ICA通常需要许多不同的输入信号来揭示混合机制。本文提出了一种基于ICA非高斯度量和遗传算法的盲去卷积和去模糊方法。该方法很简单并且不需要关于图像或模糊处理的先验知识，但是能够从单个模糊图像估计或近似模糊核。描述和讨论了各种模糊功能。该方法已经过测试，可以对不同模糊核对图像进行降级处理，并将结果与​​现有方法（如维纳滤波器，正则化滤波器和Richardson-Lucy方法）进行比较。实验结果表明，所提出的方法优于这些方法。

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