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 3276–3300 of 3874 papers

TitleStatusHype
Image Enhancement by Recurrently-trained Super-resolution Network—0
Improved Super-Resolution Convolution Neural Network for Large Images—0
Learning Resolution-Invariant Deep Representations for Person Re-Identification—0
Image Super-Resolution Using a Wavelet-based Generative Adversarial NetworkCode0
Progressive Perception-Oriented Network for Single Image Super-ResolutionCode0
Light Field Super-resolution via Attention-Guided Fusion of Hybrid LensesCode0
A neural lens for super-resolution biological imaging—0
Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems—0
Boosting Resolution and Recovering Texture of micro-CT Images with Deep Learning—0
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal imagesCode0
Coupled-Projection Residual Network for MRI Super-Resolution—0
Hybrid Residual Attention Network for Single Image Super ResolutionCode0
Super-resolution meets machine learning: approximation of measures—0
Enhanced generative adversarial network for 3D brain MRI super-resolution—0
Gated Multiple Feedback Network for Image Super-ResolutionCode0
A Comparison of Super-Resolution and Nearest Neighbors Interpolation Applied to Object Detection on Satellite Data—0
Fully Convolutional Network for Removing DCT Artefacts From Images—0
FC^2N: Fully Channel-Concatenated Network for Single Image Super-ResolutionCode0
Multi-level Wavelet Convolutional Neural NetworksCode0
MRI Super-Resolution with Ensemble Learning and Complementary Priors—0
Improving the resolution of microscope by deconvolution after dense scan—0
Distilling with Residual Network for Single Image Super Resolution—0
Deep learning in ultrasound imaging—0
Super-Resolution of PROBA-V Images Using Convolutional Neural Networks—0
Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified