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 3401–3410 of 3874 papers

TitleStatusHype
GAN-based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problems—0
Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation—0
Blind Hyperspectral-Multispectral Image Fusion via Graph Laplacian Regularization—0
Deep Learning for Image Super-resolution: A SurveyCode0
Lightweight Feature Fusion Network for Single Image Super-ResolutionCode0
Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images—0
Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity—0
Advances on CNN-based super-resolution of Sentinel-2 images—0
Progressive Generative Adversarial Networks for Medical Image Super resolution—0
DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth CompletionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified