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 3281–3290 of 3874 papers

TitleStatusHype
Volumetric Isosurface Rendering with Deep Learning-Based Super-ResolutionCode0
Unsupervised Image Noise Modeling with Self-Consistent GAN—0
Image-Adaptive GAN based ReconstructionCode0
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior—0
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression—0
Suppressing Model Overfitting for Image Super-Resolution Networks—0
Hybrid Function Sparse Representation towards Image Super ResolutionCode0
Compressed Sensing MRI via a Multi-scale Dilated Residual Convolution Network—0
Convolutional Bipartite Attractor Networks—0
A Multi-Pass GAN for Fluid Flow Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified