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 2441–2450 of 3874 papers

TitleStatusHype
Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution—0
Debiased Subjective Assessment of Real-World Image Enhancement—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving ObjectsCode1
EBSR: Feature Enhanced Burst Super-Resolution With Deformable AlignmentCode1
Perceptually-inspired super-resolution of compressed videos—0
SinIR: Efficient General Image Manipulation with Single Image ReconstructionCode1
Group-based Bi-Directional Recurrent Wavelet Neural Networks for Video Super-Resolution—0
Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV MinimizationCode1
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified