High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
Independent component analysis and nongaussianity for blind image deconvolution and deblurring
Integrated Computer-Aided Engineering
An incremental-encoding evolutionary algorithm for color reduction in images
Integrated Computer-Aided Engineering
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Spectral non-gaussianity for blind image deblurring
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
A novel blind deconvolution scheme for image restoration usingrecursive filtering
IEEE Transactions on Signal Processing
Total variation blind deconvolution
IEEE Transactions on Image Processing
Multiresolution streamline placement based on control grids
Integrated Computer-Aided Engineering
A new thresholding algorithm for document images based on the perception of objects by distance
Integrated Computer-Aided Engineering
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The principle of Independent Component Analysis ICA has been used in blind signal separation and deconvolution problems. In image restoration, such methods are often computationally intensive and ringing and noise amplification artifacts from the deblurring process greatly affect the image statistics and vary the calculated non-Gaussianity measures. To overcome the problems, we propose an enhanced scheme that employs the non-Gaussianity principle of ICA on the spectrum rather than the image data itself. That is, the spectral kurtosis is used as a measure of non-Gaussianity during the deblurring process. The deblurring process measures the non-Gaussianity of the image spectrum of the estimated images and the value maximizes at the true blurring kernel. The optimal solution is sought through a Genetic Algorithm. The scheme is simple and efficient and does not require any prior knowledge about the image or the blurring process. Validations have been carried out on various examples and they show that spectral non-Gaussianity optimizes on the parameters in a close vicinity of the original blurring functions. Results are presented for both benchmark and real images. The proposed method achieves marked improved results over the existing methods.