Sedighin Farnaz
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
J Med Signals Sens. 2024 Feb 14;14:1. doi: 10.4103/jmss.jmss_32_23. eCollection 2024.
Image enhancement, including image de-noising, super-resolution, registration, reconstruction, in-painting, and so on, is an important issue in different research areas. Different methods which have been exploited for image analysis were mostly based on matrix or low order analysis. However, recent researches show the superior power of tensor-based methods for image enhancement.
In this article, a new method for image super-resolution using Tensor Ring decomposition has been proposed. The proposed image super-resolution technique has been derived for the super-resolution of low resolution and noisy images. The new approach is based on a modification and extension of previous tensor-based approaches used for super-resolution of datasets. In this method, a weighted combination of the original and the resulting image of the previous stage has been computed and used to provide a new input to the algorithm.
This enables the method to do the super-resolution and de-noising simultaneously.
Simulation results show the effectiveness of the proposed approach, especially in highly noisy situations.
图像增强,包括图像去噪、超分辨率、配准、重建、图像修复等,是不同研究领域中的一个重要问题。已用于图像分析的不同方法大多基于矩阵或低阶分析。然而,最近的研究表明基于张量的方法在图像增强方面具有卓越的能力。
在本文中,提出了一种使用张量环分解的图像超分辨率新方法。所提出的图像超分辨率技术是为低分辨率和噪声图像的超分辨率而推导出来的。新方法基于对先前用于数据集超分辨率的基于张量的方法的修改和扩展。在该方法中,计算了前一阶段原始图像和结果图像的加权组合,并将其用作算法的新输入。
这使得该方法能够同时进行超分辨率和去噪。
仿真结果表明了所提方法的有效性,尤其是在高噪声情况下。