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 3651–3660 of 3874 papers

TitleStatusHype
FFEINR: Flow Feature-Enhanced Implicit Neural Representation for Spatio-temporal Super-Resolution—0
FFT-Enhanced Low-Complexity Near-Field Super-Resolution Sensing—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Fidelity-Naturalness Evaluation of Single Image Super Resolution—0
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images—0
Efficient Multi-Purpose Cross-Attention Based Image Alignment Block for Edge Devices—0
Test-time Cost-and-Quality Controllable Arbitrary-Scale Super-Resolution with Variable Fourier Components—0
Fine-Grained Neural Architecture Search—0
Test-time Training for Hyperspectral Image Super-resolution—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified