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 1871–1880 of 3874 papers

TitleStatusHype
Image inpainting for corrupted images by using the semi-super resolution GAN—0
Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning—0
Image Enhancement by Recurrently-trained Super-resolution Network—0
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels—0
Benchmarking Super-Resolution Algorithms on Real Data—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
Image Deconvolution with Deep Image and Kernel Priors—0
Image-based Synthesis and Re-Synthesis of Viewpoints Guided by 3D Models—0
Deep Learning Techniques for Super-Resolution in Video Games—0
IGAF: Incremental Guided Attention Fusion for Depth Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified