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 1611–1620 of 3874 papers

TitleStatusHype
A Convex Approach for Image Hallucination—0
Image Resolution Enhancement by Using Interpolation Followed by Iterative Back Projection—0
Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks—0
A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers—0
Fidelity-Naturalness Evaluation of Single Image Super Resolution—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Image Restoration by Deep Projected GSURE—0
FFT-Enhanced Low-Complexity Near-Field Super-Resolution Sensing—0
FFEINR: Flow Feature-Enhanced Implicit Neural Representation for Spatio-temporal Super-Resolution—0
Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified