SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 2551–2575 of 3874 papers

TitleStatusHype
Single Image Super-Resolution Based on Global-Local Information Synergy—0
Single Image Super-Resolution based on Wiener Filter in Similarity Domain—0
Single image super-resolution by approximated Heaviside functions—0
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission—0
Single Image Super-Resolution From Transformed Self-Exemplars—0
Single image super resolution in spatial and wavelet domain—0
Single Image Super-Resolution Methods: A Survey—0
Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels—0
Single Image Super-Resolution Using Lightweight CNN with Maxout Units—0
Single Image Super-resolution using Deformable Patches—0
Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer—0
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network—0
Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction—0
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network—0
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network—0
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis—0
Single Image Super Resolution via Manifold Approximation—0
Single Image Super-Resolution via Residual Neuron Attention Networks—0
Single Image Super Resolution - When Model Adaptation Matters—0
Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images—0
Single-Layer Learnable Activation for Implicit Neural Representation (SL^2A-INR)—0
Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network—0
Single particle algorithms to reveal cellular nanodomain organization—0
Single-photon Image Super-resolution via Self-supervised Learning—0
Single-pixel imaging based on deep learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified