Efficient blind image deconvolution using spectral non-Gaussianity

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.