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 1311–1320 of 3874 papers

TitleStatusHype
AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRICode0
Learning to Super Resolve Intensity Images from EventsCode0
Leveraging Segment Anything Model in Identifying Buildings within Refugee Camps (SAM4Refugee) from Satellite Imagery for Humanitarian OperationsCode0
A Review of Convolutional Neural Networks for Inverse Problems in ImagingCode0
Exploring Linear Attention Alternative for Single Image Super-ResolutionCode0
Learning Series-Parallel Lookup Tables for Efficient Image Super-ResolutionCode0
Conditioning and Sampling in Variational Diffusion Models for Speech Super-ResolutionCode0
Explorable Super ResolutionCode0
Learning of Patch-Based Smooth-Plus-Sparse Models for Image ReconstructionCode0
Learning Parallax Attention for Stereo Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified