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 3291–3300 of 3874 papers

TitleStatusHype
Revealing economic facts: LLMs know more than they say—0
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report—0
Revisiting L1 Loss in Super-Resolution: A Probabilistic View and Beyond—0
Neural Nearest Neighbors NetworksCode0
Extreme-scale Talking-Face Video Upsampling with Audio-Visual PriorsCode0
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive FieldsCode0
Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution MicroscopyCode0
Ultra Sharp : Study of Single Image Super Resolution using Residual Dense NetworkCode0
Neural Architecture Search for Deep Image PriorCode0
EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified