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 2001–2025 of 3874 papers

TitleStatusHype
Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution—0
Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging—0
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis—0
Learned Multi-View Texture Super-Resolution—0
Deeply Matting-based Dual Generative Adversarial Network for Image and Document Label Supervision—0
Learned super resolution ultrasound for improved breast lesion characterization—0
Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer—0
Deeply Aggregated Alternating Minimization for Image Restoration—0
Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution—0
Towards Real-world Video Face Restoration: A New Benchmark—0
Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution—0
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels—0
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix—0
Learning a Mixture of Deep Networks for Single Image Super-Resolution—0
Wider Channel Attention Network for Remote Sensing Image Super-resolution—0
Towards Robust Drone Vision in the Wild—0
Learning-Based and Quality Preserving Super-Resolution of Noisy Images—0
Learning based Deep Disentangling Light Field Reconstruction and Disparity Estimation Application—0
Learning-based Framework for US Signals Super-resolution—0
Learning-Based Quality Assessment for Image Super-Resolution—0
Deep Learning Techniques for Super-Resolution in Video Games—0
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging—0
Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model—0
Deep learning in ultrasound imaging—0
Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified