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 1141–1150 of 3874 papers

TitleStatusHype
Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution—0
HSTR-Net: Reference Based Video Super-resolution with Dual Cameras—0
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder ApproachCode1
Super resolution of histopathological frozen sections via deep learning preserving tissue structure—0
Video Super-Resolution Using a Grouped Residual in Residual Network—0
spateGAN: Spatio-Temporal Downscaling of Rainfall Fields Using a cGAN ApproachCode0
Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model—0
Image super-resolution via dynamic networkCode1
DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing—0
An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified