Acquisition of a Panorama from Several Blurred Images

In this thesis, we try to stitch one clear image with a blurred image relative to a geometric transformation, and then recover the blurred image in the meantime. Image deblurring has long been a challenging work since it is an ill-posed inverse problem. Deblurring methods using multiple or single image are both discussed in recent years. The deblurring is called blind if the kernel is unknown or non-blind if the kernel is known a priori. In order to estimate the blur kernel, we try to take the information from the non-blurred patch for help. By stitching a blurred image with a non-blurred image using Speeded-Up Robust Features (SURF), we can obtain the aligned overlapped patches. Ideally, we can estimate the blur kernel based on blurred/non-blurred patches. However, directly stitching blurred/non-blurred images leads to poor aligned patches. As a result, the kernel is misestimated and the image is incorrectly recovered. To solve this issue, a pre-deblurring as a pre-processing step of the blurred image is considered. We stitch the pre-deblurred image with the non-blurred image and record the transformation parameters for temporary. After that we stitch the original blurred image with the non-blurred image using the recorded parameters to get better-aligned patches. Now the two patches are much better-aligned than before so that the kernel can be correctly estimated. Finally, promising result using progressive inter-scale and intra-scale deconvolution is presented.