no code implementations • 12 Jan 2022 • Da He, Jiasheng Zhou, Xiaoyu Shang, Jiajia Luo, Sung-Liang Chen
In this work, we propose a deep learning-based method to remove complex noise from PAM images without mathematical priors and manual selection of settings for different input images.
1 code implementation • 27 Feb 2021 • Mengxi Liu, Qian Shi, Andrea Marinoni, Da He, Xiaoping Liu, Liangpei Zhang
The experimental results demonstrate the superiority of the proposed method, which not only outperforms all baselines -with the highest F1 scores of 87. 40% on the building change detection dataset and 92. 94% on the change detection dataset -but also obtains the best accuracies on experiments performed with images having a 4x and 8x resolution difference.
no code implementations • 3 Sep 2020 • Da He, Xiaoyu Shang, Jiajia Luo
In this work, we newly present a problem of image degradation caused by adherent mist and raindrops.
no code implementations • 8 Jun 2020 • Jiasheng Zhou, Da He, Xiaoyu Shang, Zhendong Guo, Sung-Liang Chen, Jiajia Luo
The results show that the model can enhance the image quality of the sparse PAM image of blood vessels from several aspects, which may help fast PAM and facilitate its clinical applications.
no code implementations • 7 Jul 2019 • Da He, De Cai, Jiasheng Zhou, Jiajia Luo, Sung-Liang Chen
The adaptive weighting of the patch-wise deconvolved image can eliminate patch boundary artifacts and improve deconvolved image quality.