Space-variant approaches subject to local image characteristics are useful in practical image restoration because many natural images are nonstationary. Motivated by the success of denoising approaches in the wavelet domain, we propose a region-adaptive restoration approach which adopts a wavelet denoising technique in flat regions after an under-regularized constrained least squares restoration. Experimental results verify that this approach not only improves image quality in mean square error but also contributes to ringing reduction.

A problem in image recognition in practical circumstances is that an observed image is often degraded by an imaging system. A conventional method in such a case is first to estimate the parameters of the imaging system and then restore the image before analysis. Here, we propose an alternative approach based on phase invariants in Fourier domain that needs no restoration and is fairly robust against both blur and noise. We show that the image phases in positive region of the Fourier transform of the point spread function (PSF) are blur-invariant provided that the PSF is central symmetric. Under the phase-invariant assumption, a phase correlation function between a standard image and the degraded image is used in developing the recognition algorithm. The effectiveness of this algorithm is demonstrated through experiments using ten classes of figure images from car license plates.