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 1901–1910 of 3874 papers

TitleStatusHype
Auxiliary Features-Guided Super Resolution for Monte Carlo Rendering—0
Parallel compressive super-resolution imaging with wide field-of-view based on physics enhanced network—0
PTSR: Patch Translator for Image Super-Resolution—0
Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution—0
HSTR-Net: Reference Based Video Super-resolution with Dual Cameras—0
Super resolution of histopathological frozen sections via deep learning preserving tissue structure—0
Video Super-Resolution Using a Grouped Residual in Residual Network—0
Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model—0
spateGAN: Spatio-Temporal Downscaling of Rainfall Fields Using a cGAN ApproachCode0
DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified