Two-stage filtration algorithm with interframe causal processing for multichannel image with presence of uncorrelated noise

Abstract

Introduction. When solving a number of practical problems the usage of multichannel images is common practice. Multichannel of this data permits or increases the efficiency of solving the problem, or allows to obtain useful information, which in principle cannot be extracted from the single-channel images. One of the main types of noise occurring in a multichannel image is uncorrelated noise. The optimal image filtering algorithms require enormous computational cost. Therefore, the important practical value is the synthesis of multi-channel image filtering algorithms, providing the required performance indicators at moderate computational cost.
Theoretical results. Using conditional independence properties, the expression for the a posteriori probability density of pixels at the two-stage multi-channel image filtration with causal frame processing in the presence of uncorrelated noise is obtained. Gaussian algorithm for determining the estimates of image pixels and error variance estimation with causal intra and inter-frame processing is obtained in the case of multichannel image.
Experimental results. The developed algorithm for considered example allows increasing the filtration accuracy of the sequence of homogeneous Gaussian images on a 20% - 45% compared to inter-frame averaging algorithm.
Conclusion. Optimal and quasi-two-stage multi-channel image filtration algorithms were synthesized. In algorithms the first stage is one-dimensional causal filtration along each of the coordinates, and the second is the union of the results. These algorithms allow reducing the computational cost in comparison with the optimal algorithm and thus ensuring acceptable accuracy characteristics.