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 2111–2120 of 3874 papers

TitleStatusHype
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution—0
HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation—0
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement—0
Super-Resolving Face Image by Facial Parsing InformationCode0
CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images—0
AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models—0
Acceleration-Based Kalman Tracking for Super-Resolution Ultrasound Imaging in vivo—0
Generative Diffusion Prior for Unified Image Restoration and Enhancement—0
Image-to-image domain adaptation for vehicle re-identification—0
Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified