Search Results for author: Aiping Liu

Found 11 papers, 2 papers with code

PanFlowNet: A Flow-Based Deep Network for Pan-sharpening

no code implementations ICCV 2023 Gang Yang, Xiangyong Cao, Wenzhe Xiao, Man Zhou, Aiping Liu, Xun Chen, Deyu Meng

The experimental results verify that the proposed PanFlowNet can generate various HRMS images given an LRMS image and a PAN image.

Super-Resolution

Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction

1 code implementation15 Sep 2022 Gang Yang, Li Zhang, Man Zhou, Aiping Liu, Xun Chen, Zhiwei Xiong, Feng Wu

Interpretable neural network models are of significant interest since they enhance the trustworthiness required in clinical practice when dealing with medical images.

Super-Resolution

Memory-Augmented Deep Conditional Unfolding Network for Pan-Sharpening

1 code implementation CVPR 2022 Gang Yang, Man Zhou, Keyu Yan, Aiping Liu, Xueyang Fu, Fan Wang

Pan-sharpening aims to obtain high-resolution multispectral (MS) images for remote sensing systems and deep learning-based methods have achieved remarkable success.

Denoising

Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT

no code implementations11 Dec 2021 Mengqiu Liu, Ying Liu, Yidong Yang, Aiping Liu, Shana Li, Changbing Qu, Xiaohui Qiu, Yang Li, Weifu Lv, Peng Zhang, Jie Wen

Correlations between imaging findings and clinical lab tests suggested the value of this system as a potential tool to assess disease severity of COVID-19.

Data Augmentation Lesion Segmentation

Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey

no code implementations8 Dec 2021 Xun Chen, Chang Li, Aiping Liu, Martin J. McKeown, Ruobing Qian, Z. Jane Wang

Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on non-invasively measured brain activity.

EEG Eeg Decoding

Unfolding Taylor's Approximations for Image Restoration

no code implementations NeurIPS 2021 Man Zhou, Zeyu Xiao, Xueyang Fu, Aiping Liu, Gang Yang, Zhiwei Xiong

Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image.

Image Restoration

Image De-Raining via Continual Learning

no code implementations CVPR 2021 Man Zhou, Jie Xiao, Yifan Chang, Xueyang Fu, Aiping Liu, Jinshan Pan, Zheng-Jun Zha

The proposed model is capable of achieving superior performance on both inhomogeneous and incremental datasets, and is promising for highly compact systems to gradually learn myriad regularities of the different types of rain streaks.

Continual Learning

Improving De-Raining Generalization via Neural Reorganization

no code implementations ICCV 2021 Jie Xiao, Man Zhou, Xueyang Fu, Aiping Liu, Zheng-Jun Zha

Equipped with our NR algorithm, the deep model can be trained on a list of synthetic rainy datasets by overcoming catastrophic forgetting, making it a general-version de-raining network.

Knowledge Distillation

Learning Dual Priors for JPEG Compression Artifacts Removal

no code implementations ICCV 2021 Xueyang Fu, Xi Wang, Aiping Liu, Junwei Han, Zheng-Jun Zha

Specifically, we design a variational model to formulate the image de-blocking problem and propose two prior terms for the image content and gradient, respectively.

Blocking

MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG

no code implementations17 Aug 2020 Jing Zhang, Deng Liang, Aiping Liu, Min Gao, Xiang Chen, Xu Zhang, Xun Chen

MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network.

Arrhythmia Detection

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