Over the last decades, extensive work was done on image de-blurring using different approaches. Most studies assumed that the entire image is equally distorted (space-invariant blur). In such cases, by knowing of finding the single point spread function (PSF) of the distortion, the entire image can be restored using the same distortion PSF. Various attempts have been done also to reconstruct blurred images degraded by a space-variant defocus blur. Here we assume that different areas in the image may contain different levels of Gaussian-like blur, and may include also sharp regions. Gaussian-like blur can approximate distortions such as out-of-focus and long atmospheric path. In the first step we construct a blur map by estimating edge widths at many locations in the image. We assume that the blur map resembles a depth map where the blur severity depends on the distance of the objects from the image, as occurs in limited depth-of-field imaging, focused close to the camera. In the second step the image is divided into a number of non-overlapping layers (regions) according to the blur severity. This means that in each region the blur size is within a relatively small range. Then, in each of these regions we approximate its local PSF according to a best-step-edge based method. Next, each region is de-blurred using a Total Variation reconstruction method. In the final step all the restored regions are combined into a single reconstructed image.