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 3211–3220 of 3874 papers

TitleStatusHype
Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-ResolutionCode0
Image Super-Resolution via Attention based Back Projection NetworksCode0
Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-ResolutionCode0
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion—0
Unsupervised Image Super-Resolution with an Indirect Supervised Path—0
Image Super-Resolution Improved by Edge InformationCode0
Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection—0
Kernel Modeling Super-Resolution on Real Low-Resolution ImagesCode0
Fast Image Restoration With Multi-Bin Trainable Linear UnitsCode0
Deep learning at scale for subgrid modeling in turbulent flows—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified