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 3421–3430 of 3874 papers

TitleStatusHype
Efficient Light Field Reconstruction via Spatio-Angular Dense NetworkCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
Medical Image Imputation from Image CollectionsCode0
Palantir: Towards Efficient Super Resolution for Ultra-high-definition Live StreamingCode0
Maximum Likelihood on the Joint (Data, Condition) Distribution for Solving Ill-Posed Problems with Conditional Flow ModelsCode0
Masked Autoencoders are PDE LearnersCode0
Single image super-resolution based on trainable feature matching attention networkCode0
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolutionCode0
Unifying Dimensions: A Linear Adaptive Approach to Lightweight Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified