Mosaicing and restoration from blurred image sequence taken with moving camera

A wide-area image can be synthesized from an image sequence taken with a moving camera by using image mosaicing techniques. However, motion blur caused by the motion of the camera may significantly degrade the quality of the synthesized image. In this paper, we propose a new method for generating a deblurred mosaic from an image sequence that is degraded by motion blur under the condition that we do not have any information about the intrinsic and extrinsic parameters of the moving camera during input acquisition. In this method, we assume the objects in the scene can be classified into two regions in order to handle depth. In this paper, the displacement vectors of the features, which are computed using the KLT feature tracker on the consecutive frames, are classified into two regions. Here, the classified vectors provide a Point Spread Function (PSF) of the blurred image, and a homography between two consecutive frames for segmentation and mosaicing. Experimental results show that the Signal to Noise Ratio of the generated images can be significantly improved by our proposed method.