This paper proposes a real-time digital auto-focusing algorithm using a priori estimated set of point spread functions
(PSFs). A priori set of PSFs are estimated by establishing the relation between two-dimensional PSF and onedimensional
step response whose elements are samples of profile of degraded step edge. From the priori estimated set,
the proposed auto-focusing algorithm can select the optimal PSF by the focusing criterion based on the frequency
domain analysis. We then use the constrained least square (CLS) filter to obtain the in-focused image with the estimated
optimal PSF. The proposed algorithm can be implemented in real-time because the set of PSFs are already estimated and
the filtering is performed in the frequency domain.
This paper proposes a fully digital auto-focusing algorithm for restoring the image with differently out-of-focused objects, which can restore background as well as all objects. In this paper, we assume that out-of-focus blur is isotropic such as circle of confusion (COC) or two-dimensional Gaussian blur. Therefore, the proposed algorithm can segment and estimate the point spread function (PSF) by using the size of ramp in the one-dimensional step response. The proposed algorithm can be developed by object-based image segmentation and restoration algorithm. Experimental results show that the proposed object-based image restoration algorithm can efficiently remove the space-variant out of focus blur from the image with multiple blurred objects.