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 1911–1920 of 3874 papers

TitleStatusHype
Deep learning in ultrasound imaging—0
ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution—0
Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces—0
Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution from a Blurred Image Sequence—0
Joint Face Completion and Super-resolution using Multi-scale Feature Relation Learning—0
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement—0
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network—0
Joint Flow And Feature Refinement Using Attention For Video Restoration—0
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
Bayesian Sparse Representation for Hyperspectral Image Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified