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 1751–1775 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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified