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 1776–1800 of 3874 papers

TitleStatusHype
Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm—0
Convolutional Low-Resolution Fine-Grained Classification—0
Deep Networks for Image and Video Super-Resolution—0
A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method—0
Fast Randomized-MUSIC for mm-Wave Massive MIMO Radars—0
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features—0
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Fast Online Video Super-Resolution with Deformable Attention Pyramid—0
Convolutional Bipartite Attractor Networks—0
A Comprehensive Survey of Transformers for Computer Vision—0
Image Super-Resolution using Explicit Perceptual Loss—0
Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter—0
Image Super-Resolution Using T-Tetromino Pixels—0
A Simple Plugin for Transforming Images to Arbitrary Scales—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
Controlling Neural Networks via Energy Dissipation—0
A Frequency Domain Neural Network for Fast Image Super-resolution—0
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement—0
Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation—0
Fast Image Super-Resolution Based on In-Place Example Regression—0
Contrastive Learning for Climate Model Bias Correction and Super-Resolution—0
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields—0
Fast Image Deconvolution using Hyper-Laplacian Priors—0
Joint Demosaicing and Super-Resolution (JDSR): Network Design and Perceptual Optimization—0
Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified