Geometric information of scenes available with fourdimensional (4-D) light fields (LFs) paves the way for postcapture refocusing. Light field refocusing methods proposed so far have been limited to a single planar or a volumetric region of a scene. In this paper, we demonstrate simultaneous refocusing of multiple volumetric regions in LFs. To this end, we employ a 4-D sparse finite-extent impulse response (FIR) filter consisting of multiple hyperfan-shaped passbands. We design the 4-D sparse FIR filter as an optimal filter in the least-squares sense. Experimental results confirm that the proposed filter provides 63% average reduction in computational complexity with negligible degradation in the fidelity of multi-volumetric refocused LFs compared to a 4-D nonsparse FIR filter.

The stability of two-dimensional (2-D) linear systems, continuous, discrete, and mixed (hybrid) cases with real or complex coefficients, is considered in this article using a single formalism. Algebraic necessary and sufficient conditions for testing the stability of these systems are developed. The conditions are based on the characteristic polynomial to be void of zero in the stability region which depends on the case being considered. These conditions consist of the stability test of few real univariate polynomials and a real generalized eigenvalue problem. The resulting stability test requires reduced computational complexity compared to existing techniques. A numerical example is given to illustrate the merits of the proposed approach.

In this paper an algorithm for aerial image registration is proposed. The objective of this algorithm is to register aerial images having only partial overlap, which are also geometrically distorted due to the different sensing conditions and in addition they may be contaminated with noise, may be blurred, etc. The geometric distortions considered in the registration process are rotation, translation and scaling. The proposed algorithm consists of three main steps: feature point extraction using a feature point extractor based on scale-interaction of Mexican-hat wavelets, obtaining the correspondence between the feature points of the first (reference) and the second image based on Zernike moments of neighborhoods centered on the feature points, and estimating the transformation parameters between the first and the second images using an iterative weighted least squares algorithm. Experimental results illustrate the accuracy of image registration for images with partial overlap in the presence of additional image distortions, such as noise contamination and image blurring.