Real-Time Restoration of Images Degraded by Uniform Motion Blur in Foveal Active Vision Systems

Foveated, log-polar, or space-variant image architectures provide a high resolution and wide field workspace, while providing a small pixel computation load. These characteristics are ideal for mobile robotic and active vision applications. Recently we have described a generalization of the Fourier Transform (the fast exponential chirp transform) which allows frame-rate computation of full-field 2D frequency transforms on a log-polar image format. In the present work, we use Wiener filtering, performed using the Exponential Chirp Transform, on log-polar (fovcated) image formats to de-blur images which have been degraded by uniform camera motion.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-96-C-0178); Office of Naval Research Multidisciplinary University Research Initiative (N00014-95-1-0409

Low computational complexity variable block size (VBS) partitioning for motion estimation using the Walsh Hadamard transform (WHT)

Variable Block Size (VBS) based motion estimation has been adapted in state of the art video coding, such as H.264/AVC, VC-1. However, a low complexity H.264/AVC encoder cannot take advantage of VBS due to its power consumption requirements. In this paper, we present a VBS partition algorithm based on a binary motion edge map without either initial motion estimation or Rate-Distortion (R-D) optimization for selecting modes. The proposed algorithm uses the Walsh Hadamard Transform (WHT) to create a binary edge map, which provides a computational complexity cost effectiveness compared to other light segmentation methods typically used to detect the required region

A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR

$L_1$ regularization is used for finding sparse solutions to an underdetermined linear system. As sparse signals are widely expected in remote sensing, this type of regularization scheme and its extensions have been widely employed in many remote sensing problems, such as image fusion, target detection, image super-resolution, and others and have led to promising results. However, solving such sparse reconstruction problems is computationally expensive and has limitations in its practical use. In this paper, we proposed a novel efficient algorithm for solving the complex-valued $L_1$ regularized least squares problem. Taking the high-dimensional tomographic synthetic aperture radar (TomoSAR) as a practical example, we carried out extensive experiments, both with simulation data and real data, to demonstrate that the proposed approach can retain the accuracy of second order methods while dramatically speeding up the processing by one or two orders. Although we have chosen TomoSAR as the example, the proposed method can be generally applied to any spectral estimation problems.Comment: 11 pages, IEEE Transactions on Geoscience and Remote Sensin