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 1271–1280 of 3874 papers

TitleStatusHype
FREGAN : an application of generative adversarial networks in enhancing the frame rate of videos—0
FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform—0
Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network—0
Frequency-Domain Refinement with Multiscale Diffusion for Super Resolution—0
Deep Learning for Isotropic Super-Resolution from Non-Isotropic 3D Electron Microscopy—0
Deep Learning for Inverse Problems: Bounds and Regularizers—0
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution—0
Bayesian Conditioned Diffusion Models for Inverse Problems—0
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network—0
Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified