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 3751–3775 of 3874 papers

TitleStatusHype
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural NetworkCode1
Transport-based analysis, modeling, and learning from signal and data distributions—0
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial NetworkCode1
Sampling Generative NetworksCode1
A Boosting Method to Face Image Super-resolution—0
Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution—0
Constraint matrix factorization for space variant PSFs field restorationCode0
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
Accelerating the Super-Resolution Convolutional Neural NetworkCode1
New wavelet-based superresolution algorithm for speckle reduction in SAR images—0
A Deep Primal-Dual Network for Guided Depth Super-Resolution—0
ATGV-Net: Accurate Depth Super-Resolution—0
Generic 3D Convolutional Fusion for image restoration—0
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks—0
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning—0
Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling—0
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network—0
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Scalable image coding based on epitomes—0
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior—0
RAISR: Rapid and Accurate Image Super Resolution—0
PSyCo: Manifold Span Reduction for Super Resolution—0
Needle-Match: Reliable Patch Matching Under High Uncertainty—0
Semantic-Aware Depth Super-Resolution in Outdoor Scenes—0
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified