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The paper considers the problem of multi-frame super-resolution under applicative noise which generates distributed regions of outlying observations in low resolution images. The analysis of existing solutions is performed. They include algorithms based on spin-glass models and Markov random fields used to remove applicative noise. The authors suggest their own approach, which involves using a recurrent algorithm of quasi-linear optimal filtering of a sequence of low resolution images together with superpixel segmentation performed in order to determine the regions damaged by applicative noise. The considered algorithms are compared as applied to a set of test images. The results of the experiment demonstrate that the suggested approach allows for more accurate recovery of HR images than the existing analogues.

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The article describes algorithms for multi-frame image super-resolution, which recover high-resolution images from a sequence of low-resolution images of the same scene under applicative noise. Applicative noise generates local regions of outlying observations in each image and reduces the image resolution. So far, little attention has been paid to this problem. At the same time, the use of deep neural networks is considered to be a promising method of image processing, including multi-frame image super-resolution. The article considers the existing solutions to the problem and suggests a new approach based on using several pre-trained convolutional neural networks and directed acyclic graph neural networks trained by the authors. The developed approach and the algorithms based on this approach involve iterative processing of the input sequence of low-resolution images using different neural networks at different processing stages. The stages include registration of low-resolution images, their segmentation performed in order to determine regions damaged by applicative noise, and transformation performed in order to increase the resolution. The approach combines the strengths of the existing solutions while lacking their drawbacks resulting from the use of approximate mathematical data models required for the synthesis of the image processing algorithms within the statistical theory of solutions. The experimental studies demonstrated that the suggested algorithm is fully functional and allows more accurate recovery of high-resolution images than the existing analogues.