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 1681–1690 of 3874 papers

TitleStatusHype
Detail-Enhancing Framework for Reference-Based Image Super-Resolution—0
Reference-Free Image Quality Metric for Degradation and Reconstruction Artifacts—0
Towards Real-world Video Face Restoration: A New Benchmark—0
Federated Learning for Blind Image Super-Resolution—0
One-Shot Image Restoration—0
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimationCode0
Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey—0
SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation—0
Cross-modal Diffusion Modelling for Super-resolved Spatial Transcriptomics—0
A New Multi-Picture Architecture for Learned Video Deinterlacing and Demosaicing with Parallel Deformable Convolution and Self-Attention BlocksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified