Results 51 to 60 of about 18,250,440 (204)
Single Image Super Resolution Using Deep Residual Learning
AISingle Image Super Resolution (SSIR) is an intriguing research topic in computer vision where the goal is to create high-resolution images from low-resolution ones using innovative techniques.
Moiz Hassan +2 more
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Super-resolution reconstruction for a single image based on self-similarity and compressed sensing
Journal of Algorithms & Computational Technology, 2018 Super-resolution image reconstruction can achieve favorable feature extraction and image analysis. This study first investigated the image’s self-similarity and constructed high-resolution and low-resolution learning dictionaries; then, based on sparse ...
Qiang Yang, Huajun Wang
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4× Super‐resolution of unsupervised CT images based on GAN
IET Image Processing, 2023 Improving the resolution of computed tomography (CT) medical images can help doctors more accurately identify lesions, which is important in clinical diagnosis.
Yunhe Li +3 more
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Super-resolution reconstruction of brain magnetic resonance images via lightweight autoencoder
Informatics in Medicine Unlocked, 2021 Magnetic Resonance Imaging (MRI) is useful to provide detailed anatomical information such as images of tissues and organs within the body that are vital for quantitative image analysis. However, typically the MR images acquired lacks adequate resolution
J. Andrew +6 more
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Quantitative Assessment of Single-Image Super-Resolution in Myocardial Scar Imaging
IEEE Journal of Translational Engineering in Health and Medicine, 2014 Single-image super resolution is a process of obtaining a high-resolution image from a set of low-resolution observations by signal processing. While super resolution has been demonstrated to improve image quality in scaled down images in the image ...
Hiroshi Ashikaga +4 more
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SREFBN: Enhanced feature block network for single‐image super‐resolution
IET Image Processing, 2022 Deep learning has assisted the field of single‐image super‐resolution (SR) in achieving new heights. However, the task of restoring a high‐resolution (HR) image from a highly degraded low‐resolution (LR) image is sophisticated due to poor image ...
Vachiraporn Ketsoi +3 more
doaj +1 more source
Multi‐feature fusion attention network for single image super‐resolution
IET Image Processing, 2023 Single Image Super‐Resolution algorithms have made enormous progress in recent years. However, many previous Convolution Neural Network (CNN) based Super‐Resolution algorithms only stack uniform convolution layers of fixed kernel size, and frequently ...
Jiacheng Chen +3 more
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SENext: Squeeze-and-ExcitationNext for Single Image Super-Resolution
IEEE Access, 2022 Recent research on image and video processing using convolutional neural networks has shown remarkable improvements, especially in the area of single image super-resolution(SISR). The primary target of SISR is to recover the visually appealing high-resolution (HR) output image from the original degraded low-resolution (LR) input image.
Wazir Muhammad +2 more
openaire +2 more sources
A Single-Frame Super-Resolution Innovative Approach
, 2007 Super-resolution refers to the process of obtaining a high resolution image from one or more low resolution images. In this work, we present a novel method for the super-resolution problem for the limited case, where only one image of low resolution is ...
Luz A. Torres-Méndez +5 more
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Deep Learning Based Single Image Super Resolution
, 2020 This paper talks about single image super resolution with deep learning based solution.
Harsh Vaghela (8654676)
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