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 2681–2690 of 3874 papers

TitleStatusHype
Learning-Based Quality Assessment for Image Super-Resolution—0
KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local AdjustmentCode1
Geometry Enhancements from Visual Content: Going Beyond Ground Truth—0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution—0
Detailed 3D Human Body Reconstruction from Multi-view Images Combining Voxel Super-Resolution and Learned Implicit Representation—0
Super-resolution Guided Pore Detection for Fingerprint Recognition—0
Improving the Fairness of Deep Generative Models without RetrainingCode1
Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning—0
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual NetworkCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified