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 3426–3450 of 3874 papers

TitleStatusHype
Deep Laplacian Pyramid Network for Text Images Super-ResolutionCode0
Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images—0
Learning Temporal Coherence via Self-Supervision for GAN-based Video GenerationCode1
NeuroTreeNet: A New Method to Explore Horizontal Expansion Network—0
IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network—0
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement—0
Three-dimensional Optical Coherence Tomography Image Denoising through Multi-input Fully-Convolutional Networks—0
Low-Resolution Face Recognition—0
Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound—0
Adversarial Feedback LoopCode0
Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery—0
Single Snapshot Super-Resolution DOA Estimation for Arbitrary Array Geometries—0
A Learning-Based Framework for Line-Spectra Super-resolutionCode0
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering—0
Blockwise Parallel Decoding for Deep Autoregressive ModelsCode1
Bi-GANs-ST for Perceptual Image Super-resolution—0
Functional Nonlinear Sparse Models—0
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution NetworkCode0
Neural Nearest Neighbors NetworksCode0
DeepDPM: Dynamic Population Mapping via Deep Neural Network—0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution—0
Deep learning-based super-resolution in coherent imaging systems—0
Lesion Focused Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified