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 1991–2000 of 3874 papers

TitleStatusHype
Deep multi-frame face super-resolution—0
LATIS: Lambda Abstraction-based Thermal Image Super-resolution—0
Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques—0
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution—0
Deep MR Image Super-Resolution Using Structural Priors—0
Layered Diffusion Model for One-Shot High Resolution Text-to-Image Synthesis—0
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network—0
Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization—0
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors—0
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified