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 701–725 of 3874 papers

TitleStatusHype
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
Label-Efficient Semantic Segmentation with Diffusion ModelsCode1
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
Image-specific Convolutional Kernel Modulation for Single Image Super-resolutionCode1
Pansharpening by convolutional neural networks in the full resolution frameworkCode1
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified