Nowadays, satellite images are used in various governmental applications, such as urbanization and monitoring the environment. Spatial resolution is an element of crucial impact on the usage of remote sensing imagery. As such, increasing the spatial resolution of an image is an important pre-processing step that can improve the performance of various image processing tasks, such as segmentation. Once a satellite is launched, the more practical solution to improve the resolution of its captured images is to use Single Image Super Resolution (SISR) techniques. In the recent years, Deep Convolutional Neural Networks (DCNNs) have been recognized as a highly effective tool to reconstruct a High Resolution (HR) image from its Low Resolution (LR) counterpart, which is an open problem due to the inherent difficulty of estimating the missing high frequency components. The aim of this research paper is to design and implement a satellite image SISR algorithm by estimating high frequency details through training Deep Convolutional Neural Network (DCNNs) with respect to wavelet analysis. The goal is to improve the spatial resolution of multispectral remote sensing images captured by DubaiSat-2 satellite. The accuracy of the proposed algorithm is assessed using several metrics such as Peak Signal-to-Noise Ratio (PSNR), Wavelet-based Signal-to-Noise Ratio (WSNR) and Structural Similarity Index Measurement (SSIM).
Machine learning and computer vision algorithms can provide a precise and automated interpretation of medical videos. The segmentation of the left ventricle of echocardiography videos plays an essential role in cardiology for carrying out clinical cardiac diagnosis and monitoring the patient’s condition. Most of the developed deep learning algorithms for video segmentation require an enormous amount of labeled data to generate accurate results. Thus, there is a need to develop new semi-supervised segmentation methods due to the scarcity and costly labeled data. In recent research, semi-supervised learning approaches based on graph signal processing emerged in computer vision due to their ability to avail the geometrical structure of data. Video object segmentation can be considered as a node classification problem. In this paper, we propose a new approach called GraphECV based on the use of graph signal processing for semi-supervised learning of video object segmentation applied for the segmentation of the left ventricle in echordiography videos. GraphECV includes instance segmentation, extraction of temporal, texture and statistical features to represent the nodes, construction of a graph using K-nearest neighbors, graph sampling to embed the graph with small amount of labeled nodes or graph signals, and finally a semi-supervised learning approach based on the minimization of the Sobolov norm of graph signals. The new algorithm is evaluated using two publicly available echocardiography videos, EchoNet-Dynamic and CAMUS datasets. The proposed approach outperforms other state-of-the-art methods under challenging background conditions.
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