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 1626–1650 of 3874 papers

TitleStatusHype
HiTSR: A Hierarchical Transformer for Reference-based Super-ResolutionCode0
Distilling the Knowledge from Conditional Normalizing FlowsCode0
HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion PriorCode0
Brain MRI super-resolution using 3D generative adversarial networksCode0
A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imagingCode0
Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer LearningCode0
DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise CleaningCode0
An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation NetworksCode0
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on RegressionCode0
Accurate Image Super-Resolution Using Very Deep Convolutional NetworksCode0
High-throughput, high-resolution registration-free generated adversarial network microscopyCode0
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problemsCode0
Direction-of-arrival estimation with conventional co-prime arrays using deep learning-based probablistic Bayesian neural networks—0
Directional diffusion models for graph representation learning—0
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration—0
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution—0
DIPLI: Deep Image Prior Lucky Imaging for Blind Astronomical Image Restoration—0
Boosting Resolution and Recovering Texture of micro-CT Images with Deep Learning—0
Boosting Optical Character Recognition: A Super-Resolution Approach—0
Anchored Regression Networks Applied to Age Estimation and Super Resolution—0
DiffVSR: Enhancing Real-World Video Super-Resolution with Diffusion Models for Advanced Visual Quality and Temporal Consistency—0
Diffusion Posterior Sampling is Computationally Intractable—0
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors—0
An Attention-Based Approach for Single Image Super Resolution—0
Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified