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 3251–3275 of 3874 papers

TitleStatusHype
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-ResolutionCode0
Supervised Learning Based Super-Resolution DoA Estimation Utilizing Antenna Array Extrapolation—0
Robust Online Video Super-Resolution Using an Efficient Alternating Projections Scheme—0
Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval—0
Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging—0
Robust Regression via Deep Negative Correlation Learning—0
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors—0
Progressive Face Super-Resolution via Attention to Facial LandmarkCode0
MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors—0
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution—0
RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-ResolutionCode0
Image Formation Model Guided Deep Image Super-ResolutionCode0
Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement—0
Super-resolution of Omnidirectional Images Using Adversarial LearningCode0
Jointly Aligning Millions of Images with Deep Penalised Reconstruction Congealing—0
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution—0
Architecture-aware Network Pruning for Vision Quality Applications—0
Multi-Contrast Super-Resolution MRI Through a Progressive Network—0
CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network—0
Content and Colour Distillation for Learning Image Translations with the Spatial Profile LossCode0
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement—0
Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI—0
Is There Any Recovery Guarantee with Coupled Structured Matrix Factorization for Hyperspectral Super-Resolution?—0
Benefiting from Multitask Learning to Improve Single Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified