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 1201–1225 of 3874 papers

TitleStatusHype
Fidelity-Naturalness Evaluation of Single Image Super 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
A mathematical theory of resolution limits for super-resolution of positive sources—0
Bias for Action: Video Implicit Neural Representations with Bias Modulation—0
FFEINR: Flow Feature-Enhanced Implicit Neural Representation for Spatio-temporal Super-Resolution—0
FFT-Enhanced Low-Complexity Near-Field Super-Resolution Sensing—0
Deep Spectral Prior—0
Always Look on the Bright Side of the Field: Merging Pose and Contextual Data to Estimate Orientation of Soccer Players—0
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—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
Beyond Principal Components: Deep Boltzmann Machines for Face Modeling—0
Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution—0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
DRCAS: Deep Restoration Network for Hardware Based Compressive Acquisition Scheme—0
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer—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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified