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 2951–2960 of 3874 papers

TitleStatusHype
Robust Spectral Compressed Sensing via Structured Matrix Completion—0
A Sinkhorn Regularized Adversarial Network for Image Guided DEM Super-resolution using Frequency Selective Hybrid Graph Transformer—0
Robust Super-Resolution GAN, with Manifold-based and Perception Loss—0
Super-Resolution of Real-World Faces—0
Robust Unpaired Single Image Super-Resolution of Faces—0
Robust Video Super-Resolution With Learned Temporal Dynamics—0
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models—0
Rolling Shutter Super-Resolution—0
Rotationally Equivariant Super-Resolution of Velocity Fields in Two-Dimensional Fluids Using Convolutional Neural Networks—0
A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified