We demonstrate an efficient and accelerated parallel, sparse depth reconstruction framework using compressed sensing (compressed (CS)) approximate computing. Employing data parallelism for rapid image formation, the is reconstructed from sparsely sampled scenes convex optimization. Coupled with faster imaging, this sampling reduces significantly projected laser power in active systems such as l...

An approach for the reconstruction of a building from manual measurements in a single image and a priori object information is described. The image is taken with an uncalibrated camera. Parallelism and perpendicularity of facades and building edges are examples of the object information that makes reconstruction and camera calibration from a single image possible. The reconstruction results in ...

An iterative method is presented for computed tomography imaging spectrometer (CTIS) image reconstruction in the presence of both photon noise in the image and postdetection Gaussian system noise. The new algorithm, which assumes the transfer matrix of the system has a particular structure, is evaluated experimentally with the result that it is significantly better, for larger problems, than bo...

Frame-based regularization method as one kind of sparsity representation method has been developed in recent years and has been proved to be an efficient method for CT image reconstruction. However, most of the developed CT image reconstruction methods are analysis-based frame methods. This paper proposes a novel frame-based balanced hybrid model with two sparse regularization terms for CT imag...

The reconstruction of camera orientations and structure from unordered image datasets, also known as Structure and Motion reconstruction, has become an important task for Photogrammetry. Only with few and rough initial information about the lens and the camera, exterior orientations can be derived precisely and automatically using feature extraction and matching. Accurate intrinsic orientations...