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 701–750 of 3874 papers

TitleStatusHype
Label-Efficient Semantic Segmentation with Diffusion ModelsCode1
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
Fast Neural Representations for Direct Volume RenderingCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-ResolutionCode1
Revisiting Temporal Alignment for Video RestorationCode1
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image PriorCode1
AdaDM: Enabling Normalization for Image Super-ResolutionCode1
A Practical Contrastive Learning Framework for Single-Image Super-ResolutionCode1
Rethinking the modeling of the instrumental response of telescopes with a differentiable optical modelCode1
Advancing High-Resolution Video-Language Representation with Large-Scale Video TranscriptionsCode1
Local Texture Estimator for Implicit Representation FunctionCode1
Pansharpening by convolutional neural networks in the full resolution frameworkCode1
Image-specific Convolutional Kernel Modulation for Single Image Super-resolutionCode1
Physics-Informed Neural Operator for Learning Partial Differential EquationsCode1
TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle ImagingCode1
AdaPool: Exponential Adaptive Pooling for Information-Retaining DownsamplingCode1
Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modellingCode1
Learning Continuous Representation of Audio for Arbitrary Scale Super ResolutionCode1
Scale-Aware Dynamic Network for Continuous-Scale Super-ResolutionCode1
An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural RepresentationCode1
Improving Super-Resolution Performance using Meta-Attention LayersCode1
Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networksCode1
ERQA: Edge-Restoration Quality Assessment for Video Super-ResolutionCode1
EFENet: Reference-based Video Super-Resolution with Enhanced Flow EstimationCode1
Gradient Step Denoiser for convergent Plug-and-PlayCode1
Burst Image Restoration and EnhancementCode1
Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNNCode1
A Systematic Survey of Deep Learning-based Single-Image Super-ResolutionCode1
Structure-Preserving Image Super-ResolutionCode1
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC AlgorithmCode1
Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical SystemsCode1
Exploring Separable Attention for Multi-Contrast MR Image Super-ResolutionCode1
Dual-Camera Super-Resolution with Aligned Attention ModulesCode1
Self-Attention for Audio Super-ResolutionCode1
Generalized Real-World Super-Resolution through Adversarial RobustnessCode1
Memory-Augmented Non-Local Attention for Video Super-ResolutionCode1
Transformer for Single Image Super-ResolutionCode1
edge-SR: Super-Resolution For The MassesCode1
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
Thermal Image Processing via Physics-Inspired Deep NetworksCode1
spectrai: A deep learning framework for spectral dataCode1
Light Field Image Super-Resolution with TransformersCode1
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image RescalingCode1
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-ResolutionCode1
Finding Discriminative Filters for Specific Degradations in Blind Super-ResolutionCode1
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
Fourier Series Expansion Based Filter Parametrization for Equivariant ConvolutionsCode1
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-ResolutionCode1
Self-Conditioned Probabilistic Learning of Video RescalingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified