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 191–200 of 3874 papers

TitleStatusHype
AIM 2020 Challenge on Efficient Super-Resolution: Methods and ResultsCode2
Real-World Super-Resolution via Kernel Estimation and Noise InjectionCode2
Lossless Image Compression through Super-ResolutionCode2
A Tour of Convolutional Networks Guided by Linear InterpretersCode2
Recurrent Transition Networks for Character LocomotionCode2
Image Super-Resolution Using Very Deep Residual Channel Attention NetworksCode2
IM-LUT: Interpolation Mixing Look-Up Tables for Image Super-ResolutionCode1
R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level VisionCode1
Unsupervised Imaging Inverse Problems with Diffusion Distribution MatchingCode1
DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified