ROBUST IDENTIFICATION OF MOTION AND OUT-OF-FOCUS BLUR PARAMETERS FROM BLURRED AND NOISY IMAGES

Most image deblurring methods assume knowledge of the point spread function (PSF) causing the blur. In this work we address the problem of identifying the characterizing parameter of the PSF, which corresponds to motion or out-of-focus blur, from blurred and noisy images. The observation that the spectra of these blurring functions have periodic (or almost periodic) zeros is the basis of an already known blur identification method in the cepstral domain. However, this method is found to be highly sensitive to noise. In this paper we propose the following improvements on the above method: First, adding a preprocessing stage for noise reduction, using a modified spectral subtraction approach-with a median-complement filter to estimate the noise. Second, applying an adaptive, quefrency-varying, comb-like window (lifter) in the cepstral domain to enhance the blur parameter identification. The robustness of the proposed algorithm is demonstrated by its ability to identify the blur function parameters from noisy blurred images with signal-to-noise ratio down to 0 dB for motion blur and 3 dB for out-of-focus blur, as compared to 20 dB for the Original method. © 1991.