Abstract

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.

中文翻译：

---

在应用噪声下为一系列图像构建超分辨率的方法和算法

摘要

考虑了基于在应用噪声 (AN) 条件下处理一系列低分辨率 (LR) 图像来构建多帧超分辨率 (SR) 的问题。后者在 LR 图像中以错误或异常观察的分布区域的形式出现，被认为是降低原始图像质量的附加因素，其特征是 LR 或零分辨率区域的不规则排列。使用自旋玻璃模型及其种类以及随机马尔可夫场模型来分析解决该问题的现有方法。作者描述了一种方法，该方法基于使用循环算法对一系列 LR 图像进行最佳条件线性过滤，并结合超像素分割和期望最大化聚类（EM 聚类）来识别受 AN 影响的区域。在通常和自适应设置中都考虑了条件线性滤波算法的合成，同时考虑了有关处理参数和配准装置的可能不确定性。进行了一项实验研究以比较测试图像集上的算法。对实验结果的分析表明，所开发的用于在自适应设置中构建 SR 的算法的合成方法具有一定的优势，