Results 41 to 50 of about 389,857 (275)
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution
IEEE Access, 2023 Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications.
Armin Mehri +2 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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IEEE Access, 2021 In this paper, we propose an independent neural network for single image super-resolution by residual recovery. The network is inspired by the observation that there still exists image residuals between the low-resolution image and the downsampled high ...
Fei Wang, Mali Gong
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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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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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Generative collaborative networks for single image super-resolution [PDF]
Neurocomputing, 2020 A common issue of deep neural networks-based methods for the problem of Single Image Super-Resolution (SISR), is the recovery of finer texture details when super-resolving at large upscaling factors. This issue is particularly related to the choice of the objective loss function. In particular, recent works proposed the use of a VGG loss which consists
Mohamed El Amine Seddik +2 more
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Fast Single Image Super-Resolution
, 2015 This paper addresses the problem of single image super-resolution (SR), which consists of recovering a high resolution image from its blurred, decimated and noisy version. The existing algorithms for single image SR use different strategies to handle the decimation and blurring operators.
Zhao, Ningning +5 more
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Global Learnable Attention for Single Image Super-Resolution
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Self-similarity is valuable to the exploration of non-local textures in single image super-resolution (SISR). Researchers usually assume that the importance of non-local textures is positively related to their similarity scores. In this paper, we surprisingly found that when repairing severely damaged query textures, some non-local textures with low ...
Jian-Nan Su +4 more
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Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks
Remote Sensing, 2020 Convolutional Neural Networks (CNNs) consistently proved state-of-the-art results in image Super-resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data ...
Francesco Salvetti +3 more
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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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