STATISTICAL PROCESSING OF LARGE IMAGE SEQUENCES (Accepted for publication in IEEE Trans. Image Processing)

Abstract

Abstract—The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example the Kalman and related filters, impractical. In this paper we present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 × 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modelling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus the methods of this paper may find application in modelling and single-frame estimation. I