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 1751–1800 of 3874 papers

TitleStatusHype
A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement—0
Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks—0
Acceleration-Based Kalman Tracking for Super-Resolution Ultrasound Imaging in vivo—0
Deep Unrolled Network for Video Super-Resolution—0
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution—0
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction—0
Bi-GANs-ST for Perceptual Image Super-resolution—0
Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound—0
Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations—0
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution—0
A mathematical theory of super-resolution and two-point resolution—0
Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges—0
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images—0
Bias for Action: Video Implicit Neural Representations with Bias Modulation—0
A mathematical theory of resolution limits for super-resolution of positive sources—0
Deep Spectral Prior—0
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
Always Look on the Bright Side of the Field: Merging Pose and Contextual Data to Estimate Orientation of Soccer Players—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement—0
Deep Selective Combinatorial Embedding and Consistency Regularization for Light Field Super-resolution—0
A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift—0
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution—0
Deep Sampling Networks—0
Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution—0
Beyond Principal Components: Deep Boltzmann Machines for Face Modeling—0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
Deep Residual Axial Networks—0
Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images—0
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement—0
Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey—0
Beyond MR Image Harmonization: Resolution Matters Too—0
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy—0
Deep priors for satellite image restoration with accurate uncertainties—0
All-in-One Deep Learning Framework for MR Image Reconstruction—0
Adaptive Selection of Sampling-Reconstruction in Fourier Compressed Sensing—0
Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression—0
SGSR: Structure-Guided Multi-Contrast MRI Super-Resolution via Spatio-Frequency Co-Query Attention—0
DRCAS: Deep Restoration Network for Hardware Based Compressive Acquisition Scheme—0
Image Super-Resolution Using VDSR-ResNeXt and SRCGAN—0
Image Super-Resolution Using T-Tetromino Pixels—0
Image Superresolution using Scale-Recurrent Dense Network—0
Deep Photo Cropper and Enhancer—0
Image Super-Resolution using Explicit Perceptual Loss—0
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense—0
Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection—0
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features—0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified