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SR-AFU: super-resolution network using adaptive frequency component upsampling and multi-resolution features
Image super-resolution (SR) is one of the classic computer vision tasks. This paper proposes a super-resolution network based on adaptive frequency...
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SR-USRN: learning image super-resolution with unified structure and reverse network
Using high-resolution image as reference (Ref) to recover a low-resolution (LR) image with similar texture can get the lost texture details and...
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Boundary equilibrium SR: effective loss functions for single image super-resolution
Recently, single image super-resolution (SISR) has made great progress due to the rapid development of deep convolutional neural networks (CNN), and...
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MDA-SR: Multi-level Domain Adaptation Super-Resolution for Wireless Capsule Endoscopy Images
Super-resolution (SR) of wireless capsule endoscopy (WCE) images is challenging because paired high-resolution (HR) images are not available. An...
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Deep learning-based magnetic resonance image super-resolution: a survey
Magnetic resonance imaging (MRI) is a medical imaging technique used to show anatomical structures and physiological processes of the human body. Due...
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Single image super-resolution: a comprehensive review and recent insight
Super-resolution (SR) is a long-standing problem in image processing and computer vision and has attracted great attention from researchers over the...
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SCCADC-SR: a real image super-resolution based on self-calibration convolution and adaptive dense connection
Because the real degradation model is more complex, and the different computing performance of devices leads to different degradation results. The...
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Diffusion Probabilistic Models for Underwater Image Super-Resolution
In recent years, single image super-resolution (SISR) has been extensively employed in the realm of underwater machine vision. However, the unique...
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Face super resolution based on attention upsampling and gradient
Face Super-Resolution(SR) is a specific domain SR task, which is to reconstruct low-resolution(LR) face images. Recently, many face super-resolution...
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Blind visual quality assessment for super-resolution images: database and model
Image super-resolution (SR) algorithms are placed on high hope to reconstruct ultra-high-definition (UHD) videos from existing low-resolution videos....
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Image super resolution boosting using beta wavelet
Image super resolution (SR) is a critical category within the field of image processing techniques that aims to improve the resolution of both images...
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Enhanced pyramidal residual networks for single image super-resolution
Several super-resolution (SR) techniques are introduced in the literature, including traditional and machine learning-based algorithms. Especially,...
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Multi-frame spatio-temporal super-resolution
Increasing the resolution of digital images and videos using digital super-resolution (SR) techniques has been of great interest in industry and...
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Image Super-Resolution with Deep Variational Autoencoders
Image super-resolution (SR) techniques are used to generate a high-resolution image from a low-resolution image. Until now, deep generative models...
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A review of single image super-resolution reconstruction based on deep learning
Single image super-resolution (SISR) is an important research field in computer vision, the purpose of which is to recover clear, high-resolution...
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Audio super-resolution via vision transformer
Audio super-resolution refers to techniques that improve the audio signals quality, usually by exploiting bandwidth extension methods, whereby audio...
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HST: Hierarchical Swin Transformer for Compressed Image Super-Resolution
Compressed Image Super-resolution has achieved great attention in recent years, where images are degraded with compression artifacts and...
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Attention hierarchical network for super-resolution
Deep neural networks with attention mechanism for super-resolution (SR) have achieved good SR performance by focusing on the high-frequency...
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Infrared Image Super-Resolution via GAN
The ability of generative models to accurately fit data distributions has resulted in their widespread adoption and success in fields such as...
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Superpixel Driven Unsupervised Deep Image Super-Resolution
Most of the existing deep learning-based image super-resolution methods require a large number of datasets or ground truth. However, these methods...
