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 3231–3240 of 3874 papers

TitleStatusHype
Architecture-aware Network Pruning for Vision Quality Applications—0
Multi-Contrast Super-Resolution MRI Through a Progressive Network—0
CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network—0
Content and Colour Distillation for Learning Image Translations with the Spatial Profile LossCode0
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement—0
Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI—0
Is There Any Recovery Guarantee with Coupled Structured Matrix Factorization for Hyperspectral Super-Resolution?—0
Benefiting from Multitask Learning to Improve Single Image Super-Resolution—0
Improved Super-Resolution Convolution Neural Network for Large Images—0
Image Enhancement by Recurrently-trained Super-resolution Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified