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 2161–2170 of 3874 papers

TitleStatusHype
Training a Task-Specific Image Reconstruction Loss—0
Cutting-Edge Techniques for Depth Map Super-Resolution—0
CUF: Continuous Upsampling Filters—0
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning—0
Cuboid-Net: A Multi-Branch Convolutional Neural Network for Joint Space-Time Video Super Resolution—0
CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution—0
CT Super Resolution via Zero Shot Learning—0
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)—0
CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution—0
Localisation And Imaging Methods for Moving Target Ghost Imaging Radar Based On Correlation Intensity Weighting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified