A novel spatio-temporal filter is described for monochrome image sequences with either signal-independent or signal-dependent noise by considering both spatial and temporal correlations. With the assumptions of spatio-temporal separability and temporal stationarity, it is shown that motion-compensated groups of frames can be decorrelated by using the Karhunen-Loeve transform. Practical filters that work well on a variety of image sequences are developed by first applying the Hadamard transform along the temporal direction. Subsequently, the parametric adaptive Wiener filter is applied to each of the resulting approximately decorrelated transformed images. These transformed images are classified into one average image and a remaining set of residual images, which provide interesting and useful interpretations of the type of image sequence. The filter performance is evaluated by considering different types of image sequences in the database. The procedure advanced for processing a sequence of monochrome images can be adapted for generalization to multispectral images and this possibility is currently under detailed investigation.

The need for approximating block-Toeplitz with Toeplitz block matrices by means of block-circulant with circulant block matrices with the objective of transforming an inherently ill-posed image deconvolution problem to a well-posed one motivated a surge of papers on the analysis of the quality as well as the speed of convergence of the algorithms that produce such approximants to serve as preconditioners for conjugate-gradient methods. This correspondence contributes to that surge by giving a simple proof of the fact that the sequence of eigenvalues of the Hermitian block Toeplitz with Toeplitz-block matrices are asymptotically equidistributed. To do this, Weyl's (1916) results on the distribution properties of multidimensional sequences are exploited. Inferences to related results are made.

A procedure is advanced to restore a single color image, which has been degraded by a linear shift-invariant blur in the presence of additive stationary noise. Four sensors are needed, followed by the application of the RGB-to-YIQ transformation. Subsequently, one three-dimensional (3D) Wiener filter on a sequence of two luminance component images and two two-dimensional (2D) Wiener filters on each of the two chrominance component images are needed. For the procedure to be successful, the imposition of a strongly coprime condition on the wavenumber response of two distinct sensor blur functions is necessary. The resulting well-conditioned problem is shown to provide improved restoration over the decorrelated component and the independent channel restoration methods, each of which uses one sensor for each of the three primary color components.

A procedure is advanced to restore a single color image, which has been degraded by a linear shift-invariant blur in the presence of additive noise. Four sensors are needed, followed by the application of the RGB-YIQ transformation. Subsequently, one 3-D Wiener filtration is required on a sequence of two luminance components and a 2-D Wiener filter is needed on each of the two chrominance components. For the procedure to be successful, the imposition of a strongly coprime condition on the wavenumber response of two distinct sensor blur functions is necessary. The resulting well-conditioned problem is shown to provide high-quality color images.