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 2901–2910 of 3874 papers

TitleStatusHype
Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution ImagingCode0
SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis—0
SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias ClassificationCode0
Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution—0
EVRNet: Efficient Video Restoration on Edge Devices—0
Decomposition, Compression, and Synthesis (DCS)-based Video Coding: A Neural Exploration via Resolution-Adaptive Learning—0
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution—0
Model Adaptation for Inverse Problems in Imaging—0
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images—0
Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified