A new localized and computationally efficient approach is presented for shift/space-variant image restoration. Unlike conventional approaches, it models shift-variant blurring in a completely local form based on the recently proposed Rao Transform (RT). RT facilitates almost exact inversion of the blurring process locally and permits very fine-grain parallel implementation. The new approach naturally exploits the spatial locality of blurring kernels and smoothness of underlying focused images. It formulates the deblurring problem in terms of local parameters that are less correlated than raw image data. It is a fundamental advance that is general and not limited to any specific form of the blurring kernel such as a Gaussian. It has significant theoretical and computational advantages in comparison with conventional approaches such as those based on Singular Value Decomposition of blurring kernel matrices. Experimental results are presented for both synthetic and real image data. This approach is also relevant to solving integral equations.
A new passive ranging technique named Robust Depth-from-Defocus (RDFD) is presented for autofocusing in digital cameras. It is adapted to work in the presence of image shift and scale change caused by camera/hand/object motion. RDFD is similar to spatial-domain Depth-from-Defocus (DFD) techniques in terms of computational efficiency, but it does not require pixel correspondence between two images captured with different defocus levels. It requires approximate correspondence between image regions in different image frames as in the case of Depth-from-Focus (DFF) techniques. Theory and computational algorithm are presented for two different variations of RDFD. Experimental results are presented to show that RDFD is robust against image shifts and useful in practical applications. RDFD also provides insight into the close relation between DFF and DFD techniques.
It is known froiii goiiretric optics that aiiy system of lenses can be approxiined by a systciii which realizes a perspective projection of a 3D scene oiito a 2D plarie.Projective geometry is an efficient tool to invistigate such image forming system. This paper treates the problem ol calibrating cameraS using this tool. In our work, a new formulation of calibrating a moving camera is preseiited, and is based on the absolute conic properties. The projective invariance of absolute conic under rigid motion ensurcs h a t its image is independant of camera position, and depends only on the intrinsic parameters of the camera. The camera calibration is computed in two steps. First, the estimation of the coeficients of the epipolar triursforniation is estmated. The computation of the absolute conic image is based on the pole and polar of the conic. Theii, relalions between intrinsic parameters and cocfiicienls of absolutc conic image are then deduced. Both compukr-generated and real data are included in experiments, which illustrals how our method works. This technique is integrated in an ikrative filtring scheme.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.