Digital Image Processing
ICA and genetic algorithms for blind signal and image deconvolution and deblurring
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
An adaptive Gaussian model for satellite image deblurring
IEEE Transactions on Image Processing
Bypass methods for constructing robust automatic human tracking system
Integrated Computer-Aided Engineering
A soft computing method for detecting lifetime building thermal insulation failures
Integrated Computer-Aided Engineering
An incremental-encoding evolutionary algorithm for color reduction in images
Integrated Computer-Aided Engineering
Spectral non-gaussianity for blind image deblurring
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Efficient blind image deconvolution using spectral non-Gaussianity
Integrated Computer-Aided Engineering
2D and 3D palmprint information, PCA and HMM for an improved person recognition performance
Integrated Computer-Aided Engineering
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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.