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 3291–3300 of 3874 papers

TitleStatusHype
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning—0
AIM 2020 Challenge on Video Temporal Super-Resolution—0
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results—0
Structure Tensor Based Image Interpolation Method—0
Studying Very Low Resolution Recognition Using Deep Networks—0
Study of Subjective and Objective Quality in Super-Resolution Enhanced Broadcast Images on a Novel SR-IQA Dataset—0
Exploiting Style and Attention in Real-World Super-Resolution—0
StyleDEM: a Versatile Model for Authoring Terrains—0
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results—0
StyleGAN-induced data-driven regularization for inverse problems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified