Methods and Algorithms for Constructing Super Resolutionfor a Sequence of Images under Applicative Noise

The problem of constructing multiframe superresolution (SR) based on processing a sequence of low-resolution (LR) images in conditions of applicative noise (AN) is considered. The latter appear in the form of distributed areas of false or anomalous observations in LR images and are considered as an additional factor in reducing the quality of the original images, characterized by an irregular arrangement of LR or zero-resolution areas. The existing methods for solving this problem are analyzed using models of spin glasses and their varieties, as well as models of random Markov fields. The authors describe a method based on the use of recurrent algorithms for the optimal conditional linear filtering of a sequence of LR images in combination with superpixel segmentation and Expectation-Maximization-clustering (EM-clustering) to identify areas affected by AN. The synthesis of conditionally linear filtering algorithms is considered both in the usual and in the adaptive setting, taking into account the possible uncertainty regarding the processing parameters and registration means. An experimental study is carried out to compare algorithms on sets of test images. The analysis of the experimental results shows certain advantages of the developed approach for the synthesis of algorithms for constructing SR in an adaptive setting, which consists in increasing the accuracy and structural similarity of high-resolution (HR) image restoration in comparison with analogs.