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 3076–3100 of 3874 papers

TitleStatusHype
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution—0
Learned Multi-View Texture Super-Resolution—0
Neural Architecture Search for Deep Image PriorCode0
Segmentation and Generation of Magnetic Resonance Images by Deep Neural NetworksCode0
Fast Adaptation to Super-Resolution Networks via Meta-LearningCode1
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation—0
Deep Video Super-Resolution using HR Optical Flow EstimationCode1
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer—0
Hyperspectral Super-Resolution via Coupled Tensor Ring Factorization—0
End-To-End Trainable Video Super-Resolution Based on a New Mechanism for Implicit Motion Estimation and Compensation—0
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI ScansCode1
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video ProcessingCode0
Enforcing Physical Constraints in Neural Neural Networks through Differentiable PDE LayerCode0
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES—0
HighRes-net: Multi-Frame Super-Resolution by Recursive FusionCode1
Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database—0
Characteristic Regularisation for Super-Resolving Face Images—0
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks—0
Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution—0
CNN-generated images are surprisingly easy to spot... for nowCode1
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network—0
Rapid Whole-Heart CMR with Single Volume Super-resolution—0
Exploiting Style and Attention in Real-World Super-Resolution—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—0
Anisotropic Super Resolution in Prostate MRI using Super Resolution Generative Adversarial Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified