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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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Deep Learning-Based Single-Image Super-Resolution: A Comprehensive Review
IEEE Access, 2023 High-fidelity information, such as 4K quality videos and photographs, is increasing as high-speed internet access becomes more widespread and less expensive.
Karansingh Chauhan +7 more
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Super-resolution fluorescence imaging with single molecules [PDF]
Current Opinion in Structural Biology, 2013 The ability to detect, image and localize single molecules optically with high spatial precision by their fluorescence enables an emergent class of super-resolution microscopy methods which have overcome the longstanding diffraction barrier for far-field light-focusing optics.
Steffen J, Sahl, W E, Moerner
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Single-shot super-resolution quantitative phase imaging allowed by coherence gate shaping
, 2023 Biomedical and metasurface researchers repeatedly reach for quantitative phase imaging (QPI) as their primary imaging technique due to its high-throughput, label-free, quantitative nature. So far, very little progress has been made toward achieving super-
Petr Bouchal +5 more
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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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Image Super-Resolution with Adversarial Learning [PDF]
, 2022 Single-image super-resolution refers to the problem of generating a high-resolution image from a low-resolution one. In this work we address to the problem of single-image super-resolution of degraded low-resolution images.
Boem, Davide
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Deep Back-ProjectiNetworks for Single Image Super-Resolution [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Previous feed-forward architectures of recently proposed deep super-resolution networks learn the features of low-resolution inputs and the non-linear mapping from those to a high-resolution output. However, this approach does not fully address the mutual dependencies of low- and high-resolution images.
Muhammad Haris 0002 +2 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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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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