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 1781–1790 of 3874 papers

TitleStatusHype
Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors—0
A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models—0
Efficient Image Super-Resolution using Vast-Receptive-Field AttentionCode1
Deep Fourier Up-SamplingCode0
DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images—0
Learning Texture Transformer Network for Light Field Super-Resolution—0
Cost-effective photonic super-resolution millimeter-wave joint radar-communication system using self-coherent detection—0
Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images—0
Flexible Alignment Super-Resolution Network for Multi-Contrast MRICode0
A Simple Plugin for Transforming Images to Arbitrary Scales—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified