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 1931–1940 of 3874 papers

TitleStatusHype
On the modern deep learning approaches for precipitation downscaling—0
Exploring the solution space of linear inverse problems with GAN latent geometry—0
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics—0
Theoretical Perspectives on Deep Learning Methods in Inverse Problems—0
GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans—0
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution—0
A Fast Text-Driven Approach for Generating Artistic Content—0
Localisation And Imaging Methods for Moving Target Ghost Imaging Radar Based On Correlation Intensity Weighting—0
Multi-Modality Image Super-Resolution using Generative Adversarial NetworksCode0
Multi-scale Super-resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified