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 2431–2440 of 3874 papers

TitleStatusHype
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI—0
Improved detection of small objects in road network sequences—0
Improved Super-Resolution Convolution Neural Network for Large Images—0
Improved Super Resolution of MR Images Using CNNs and Vision Transformers—0
Improving Clinical Diagnosis Performance with Automated X-ray Scan Quality Enhancement Algorithms—0
Improving Generative Adversarial Networks for Video Super-Resolution—0
Improving Multi-View Stereo via Super-Resolution—0
Improving Object Detection Quality in Football Through Super-Resolution Techniques—0
Improving the resolution of microscope by deconvolution after dense scan—0
Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified