A blind image restoration for non-linear motion blurs with non-uniform point spread functions based on multiple blurred versions of a same scene is proposed. The restoration is separately considered as identification and deconvolution problems. In the proposed identification process, an identification difficulty is introduced to rank an order of blur identification. A blurred image with the lowest identification difficulty is initially identified by using a single-image-based scheme. Then, other images are identified based on a cross convolution relation between each pair of blurred images. In addition, an iterative feedback scheme is applied to improve the identification results. For the deconvolution process, a spatial adaptive scheme using regional optimal terminating points is modified from a conventional iterative deconvolution scheme. The images are decomposed into sub-regions based on smoothness. The regional optimal terminating points are independently assigned to suppress a noise in smooth regions and sharpen the image in edgy regions. The optimal terminating point for each region is decided by considering a discrepancy error. Restoration examples of simulated and real world blurred images are experimented to demonstrate the performance of the proposed method.

This paper describes a new feature extraction model (Active Model) which is extended from the active contour model (Snakes). Active Model can be applied to various energy minimizing models since it integrates most of the energy terms ever proposed into one model and also provides the new terms for multiple images such as motion and stereo images. The computational order of energy minimization process is estimated in comparison with a dynamic programming method and a greedy algorithm, and it is shown that the energy minimization process in Active Model is faster than the others. Some experimental results are also shown.

This paper presents a new approach to the recovery of 3-D structure from multiple pairs of images from different viewpoints. Searching for the corresponding points between images, which is common in stereopsis, is avoided. Extracted edges from input images are projected back into 3-D space, and their intersections are calculated directly. Many false intersections may appear, but if we have many pair images, true intersections are extracted by appropriate thresholding. Octree representation of the intersections enables this approach. We consider a way to treat adjacent edge piexels as a line segment rather than as individual points, which differs from previous works and leads to a new algorithm. Experimental results using both synthetic and actual images are also described.