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

Showing 12011210 of 3874 papers

TitleStatusHype
MemNet: A Persistent Memory Network for Image RestorationCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
Deep Hyperspectral Prior: Denoising, Inpainting, Super-ResolutionCode0
Medical Image Imputation from Image CollectionsCode0
Deep Generative Model based Rate-Distortion for Image Downscaling AssessmentCode0
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus ImagingCode0
Deep Fourier Up-SamplingCode0
Maximum Likelihood on the Joint (Data, Condition) Distribution for Solving Ill-Posed Problems with Conditional Flow ModelsCode0
Masked Autoencoders are PDE LearnersCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41Unverified