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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3301–3325 of 3874 papers

TitleStatusHype
Medical image super-resolution method based on dense blended attention network—0
Zoom To Learn, Learn To ZoomCode1
Ensemble Super-Resolution with A Reference DatasetCode0
A Cone-Beam X-Ray CT Data Collection designed for Machine LearningCode0
Handheld Multi-Frame Super-ResolutionCode0
EDVR: Video Restoration with Enhanced Deformable Convolutional NetworksCode1
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution—0
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution PipelineCode0
Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning—0
SinGAN: Learning a Generative Model from a Single Natural ImageCode1
Joint High Dynamic Range Imaging and Super-Resolution from a Single ImageCode0
3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation—0
Multi-level Encoder-Decoder Architectures for Image Restoration—0
Maximum a Posteriori on a Submanifold: a General Image Restoration Method with GAN—0
Label super-resolution networks—0
Understanding Opportunities for Efficiency in Single-image Super Resolution Networks—0
HIGHLY EFFICIENT 8-BIT LOW PRECISION INFERENCE OF CONVOLUTIONAL NEURAL NETWORKS—0
An approach to image denoising using manifold approximation without clean images—0
Unsupervised and Unregistered Hyperspectral Image Super-Resolution with Mutual Dirichlet-NetCode0
Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR ApplicationsCode0
Multi-scale deep neural networks for real image super-resolutionCode0
Super-Resolved Image Perceptual Quality Improvement via Multi-Feature Discriminators—0
Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks—0
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels—0
Feature Forwarding for Efficient Single Image DehazingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified