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 1041–1050 of 3874 papers

TitleStatusHype
Bridging Component Learning with Degradation Modelling for Blind Image Super-ResolutionCode1
Dual-Camera Super-Resolution with Aligned Attention ModulesCode1
NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator DiffusionCode1
Dual-Stage Approach Toward Hyperspectral Image Super-ResolutionCode1
Dual Super-Resolution Learning for Semantic SegmentationCode1
Hypernetwork-Based Adaptive Image RestorationCode1
Parallel and Flexible Sampling from Autoregressive Models via Langevin DynamicsCode1
Dynamic Implicit Image Function for Efficient Arbitrary-Scale Image RepresentationCode1
B-Spline Texture Coefficients Estimator for Screen Content Image Super-ResolutionCode1
Ship in Sight: Diffusion Models for Ship-Image Super ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified