Correct spatially varying image blur by Projective Motion Richardson-Lucy Algorithm and Blur Image alignment

Abstract

Blur is a typical image artifact when pictures are taken with long exposure or with moving objects. A lot of research has been carried out on the topic of image deblurring. Many methods assume each pixel in the image undergoes the same amount of blur. However, the relative motion between a camera and the scene often causes spatially varying blur which is different at every pixel. Although it is observed that most of the image blurs are spatially variant in recent works (1), there is no existing model to represent or to reduce spatially varying blurs. This thesis addresses the problem of modeling and correcting spatially varying image blurs caused by rigid camera motion. It first presents a new Projective Motion Blur Model which models a blurred image as an integration of a sequence of projectively transformed clear images. These projective transformations describe the camera?s motion during exposure. This formulation is derived according to the physical cause of the blurring effect and also offers a compact representation of the spatially varying blur. Subsequently, we propose the Projective Motion Richardson-Lucy (RL) algorithm to recover a clear image from an image undergoing spatially varying blur. We also incorporated state-of-the-art regularization image priors to improve deblurring results. We further investigated the deblurring problem when multiple blurred images of the same scene are available. To make use of complementary information in different images, these blurred images must be registered to each other. However, existing image registration algorithms only apply on clear images. Hence, we propose a method to align blurred images. The algorithm in this thesis uses frequency domain properties of the blurred images for alignment, which is both efficient and effective. The key feature of this method is that it could align motion blurred images.