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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2381–2390 of 3874 papers

TitleStatusHype
Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform—0
Multi-modal Datasets for Super-resolution—0
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing—0
Multimodal Deep Unfolding for Guided Image Super-Resolution—0
Multi-modal Facial Action Unit Detection with Large Pre-trained Models for the 5th Competition on Affective Behavior Analysis in-the-wild—0
Multi-modal Image Processing based on Coupled Dictionary Learning—0
Multimodal Image Super-resolution via Deep Unfolding with Side Information—0
TSP-Mamba: The Travelling Salesman Problem Meets Mamba for Image Super-resolution and Beyond—0
Compressing Deep Image Super-resolution Models—0
A comparative study of paired versus unpaired deep learning methods for physically enhancing digital rock image resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified