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 531–540 of 3874 papers

TitleStatusHype
LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction—0
Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEsCode1
Study of Subjective and Objective Quality in Super-Resolution Enhanced Broadcast Images on a Novel SR-IQA Dataset—0
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate DataCode0
AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content—0
Degradation-Guided One-Step Image Super-Resolution with Diffusion PriorsCode3
NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results—0
Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial—0
Denoising Graph Super-Resolution towards Improved Collider Event ReconstructionCode0
Compressed Depth Map Super-Resolution and Restoration: AIM 2024 Challenge Results—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified