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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2601–2610 of 3874 papers

TitleStatusHype
Sparsity-Aware Optimal Transport for Unsupervised Restoration Learning—0
Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints—0
Sparsity-Based Super Resolution for SEM Images—0
Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models—0
Spatial-Angular Representation Learning for High-Fidelity Continuous Super-Resolution in Diffusion MRI—0
Spatial Degradation-Aware and Temporal Consistent Diffusion Model for Compressed Video Super-Resolution—0
Spatial Frequency Bias in Convolutional Generative Adversarial Networks—0
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution—0
Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning—0
Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified