Abstract
In most of the real life applications of imaging, the Point Spread Functions (PSFs) responsible for degrading the observed images are not known. Hence, PSF and image must be identified from the observed noisy blurred image. Here, we present a computationally simple iterative blind deconvolution algorithm that alternates between Fourier and spatial domain. During the iteration, the algorithm imposes some constraints making the results more accurate and visually more appealing than existing schemes. This method delivers good results for images having uniform as well as non-uniform background intensity. Experimental results are provided to validate the performance of the proposed scheme.
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