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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3226–3250 of 3874 papers

TitleStatusHype
A Tour of Convolutional Networks Guided by Linear InterpretersCode2
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
Improved Super-Resolution Convolution Neural Network for Large Images—0
Image Enhancement by Recurrently-trained Super-resolution Network—0
Learning Resolution-Invariant Deep Representations for Person Re-Identification—0
Image Super-Resolution Using a Wavelet-based Generative Adversarial NetworkCode0
Progressive Perception-Oriented Network for Single Image Super-ResolutionCode0
Light Field Super-resolution via Attention-Guided Fusion of Hybrid LensesCode0
Learned Image Downscaling for Upscaling using Content Adaptive ResamplerCode1
A neural lens for super-resolution biological imaging—0
Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems—0
Boosting Resolution and Recovering Texture of micro-CT Images with Deep Learning—0
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal imagesCode0
Coupled-Projection Residual Network for MRI Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified