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 481–490 of 3874 papers

TitleStatusHype
Conffusion: Confidence Intervals for Diffusion ModelsCode1
Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIsCode1
Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face RecognitionCode1
Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-ResolutionCode1
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
Conditional Simulation Using Diffusion Schrödinger BridgesCode1
Dynamic Implicit Image Function for Efficient Arbitrary-Scale Image RepresentationCode1
Conditional Hyper-Network for Blind Super-Resolution with Multiple DegradationsCode1
DynaVSR: Dynamic Adaptive Blind Video Super-ResolutionCode1
Conditional Variational Diffusion ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified