Classical image restoration is mostly base on the image deconvolution under the assumptions of linear system
transformation, stationary signal statistics and stationary, signal independent noise. Unfortunately, the assumptions are
not always accurate in real problems. For example, the optical aberrations, local defocus, local motion blur, temperature
variation, flexible medium, and non-stationary platform all cause the uncertain different degradation in different area of
the images. Therefore, overlapping-region sectioned restoration is suggested to reconstruct such blurred images with
space-variant point spread function (SVPSF). First of all, the full image is divided into several sub-sections, in which the
PSF nominally space invariant (SI). After the restoration with SI algorithm, the sub-frames are spliced to construct the
composite full-frame. Moreover, overlapping extension is employed to isolate edge-ringing effects from circular
convolution between the different restored sub-frames. In this paper, with the help of SSIM (Structural Similarity) and
GRM (Gradient Ringing Matrix) image quality assessment approaches, we discussed the selection of overlapping region
of the sectioned restoration with different algorithms, for images with signal to noise ratio (SNR) from 25db to 40db.
Our investigation proves that the restored image quality is best when the overlapping region as wide as the energydistribution-
area of degradation function.