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 811–820 of 3874 papers

TitleStatusHype
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-ResolutionCode2
SwinFuSR: an image fusion-inspired model for RGB-guided thermal image super-resolutionCode2
SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation—0
Single-sample image-fusion upsampling of fluorescence lifetime images—0
A New Multi-Picture Architecture for Learned Video Deinterlacing and Demosaicing with Parallel Deformable Convolution and Self-Attention BlocksCode0
Cross-modal Diffusion Modelling for Super-resolved Spatial Transcriptomics—0
Partial Large Kernel CNNs for Efficient Super-ResolutionCode2
VideoGigaGAN: Towards Detail-rich Video Super-Resolution—0
Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-ResolutionCode2
Little Pilot is Needed for Channel Estimation with Integrated Super-Resolution Sensing and Communication—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified