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 3601–3610 of 3874 papers

TitleStatusHype
Fast and Light-Weight Network for Single Frame Structured Illumination Microscopy Super-Resolution—0
Efficient Video Super-Resolution for Real-time Rendering with Decoupled G-buffer Guidance—0
Temporal and Spatial Super Resolution with Latent Diffusion Model in Medical MRI images—0
Fast and selective super-resolution ultrasound in vivo with sono-switchable nanodroplets—0
WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images—0
Efficient Two-Dimensional Line Spectrum Estimation Based on Decoupled Atomic Norm Minimization—0
Wavelet-Based Segmentation on the Sphere—0
EfficientTempNet: Temporal Super-Resolution of Radar Rainfall—0
Fast Image Deconvolution using Hyper-Laplacian Priors—0
Diffusion Models, Image Super-Resolution And Everything: A Survey—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified