Development of image restoration techniques

Abstract

Image denoising and image deblurring are studied as part of the thesis. In deblurring, blind deconvolution is investigated. Out of the several classes of blind deconvolution techniques, Non parametric Methods based on Image Constraints are studied at greater depth. A new algorithm based on the Iterative Blind Deconvolution(IBD) technique is developed. The algorithm makes use of spatial domain constraints of non-negativity and support. The Fourier-domain constraint may be described as constraining the product of the Fourier spectra of the image f and the Fourier spectra of the point spread function h to be equal to the convolution spectrum. Within each iteration, the algorithm switches between spatial domain and frequency domain and imposes known constraints on each. The convergence of the original IBD can be accelerated by limiting high magnitude values in frequency domain of both estimated image and point spread function. The new algorithm converges within less than 25 iterations where as the original IBD took nearly 500 iterations. Inclusion of the support constraint in the spatial domain improves quality of the restored image. Also, sum of the spatial domain values of the point spread function should be made equal to one at the end of each iteration, for preventing the loss of image intensity. PSNR values calculated for restored images show signi¯cant improvement in image quality. A PSNR of 17.8dB is obtained for the improved scheme where as it is 14.3dB for the original IBD. A new stopping criteria based on standard deviation of the image power for last k iterations is de¯ned for stopping the algorithm when it converges. In denoising, an edge retrieval technique is developed which preserves the image details along with e®ectively removing impulse noise. Noisy pixels are detected in the ¯rst phase and in the next phase those pixel values are replaced with an estimate of the actual value. For dealing with the wrong classi¯cation of edge pixels as noisy pixels, an edge retrieval technique based on pixel-wise MAD is de¯ned. This scheme retrieves the pixels which are wrongly classi¯ed as noise. The algorithm gives high PSNR values as well as very good detail preservation.