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 1051–1075 of 3874 papers

TitleStatusHype
An End-to-end Framework For Low-Resolution Remote Sensing Semantic SegmentationCode1
Fast Monte Carlo Rendering via Multi-Resolution SamplingCode1
Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous DatasetsCode1
Does Diffusion Beat GAN in Image Super Resolution?Code1
Learning Detail-Structure Alternative Optimization for Blind Super-ResolutionCode1
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial NetworkCode1
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
Anchored Regression Networks Applied to Age Estimation and Super Resolution—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
Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime—0
DiffVSR: Enhancing Real-World Video Super-Resolution with Diffusion Models for Advanced Visual Quality and Temporal Consistency—0
An Attention-Based Approach for Single Image Super Resolution—0
Diffusion Posterior Sampling is Computationally Intractable—0
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors—0
Anatomically Guided Motion Correction for Placental IVIM Parameter Estimation with Accelerated Sampling Method—0
Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial—0
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution—0
360-Degree Video Super Resolution and Quality Enhancement Challenge: Methods and Results—0
EPS: Efficient Patch Sampling for Video Overfitting in Deep Super-Resolution Model Training—0
Diffusion Image Prior—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified