2024/09/08 by Jianxin Xie, Wonhee Ko, Xie, Jianxin +5 · 1 citation
Computer Science · Earth and Planetary Sciences · Engineering · #FOS: Electrical engineering #Image Processing Techniques and Applications #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #Seismic Imaging and Inversion Techniques #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2409.05118
openalex publication_date 2024/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Image denoising is a critical task in various scientific fields such as medical imaging and material characterization, where the accurate recovery of underlying structures from noisy data is essential. Although supervised denoising techniques have achieved significant advancements, they typically require large datasets of paired clean-noisy images for training. Unsupervised methods, while not reliant on paired data, typically necessitate a set of unpaired clean images for training, which are not always accessible. In this paper, we propose a physics-augmented deep learning with adversarial domain adaption (PDA-Net) framework for unsupervised image denoising, with applications to denoise real-world scanning tunneling microscopy (STM) images. Our PDA-Net leverages the underlying physics to simulate and envision the ground truth for denoised STM images. Additionally, built upon Generative Adversarial Networks (GANs), we incorporate a cycle-consistency module and a domain adversarial module into our PDA-Net to address the challenge of lacking paired training data and achieve information transfer between the simulated and real experimental domains. Finally, we propose to implement feature alignment and weight-sharing techniques to fully exploit the similarity between simulated and real experimental images, thereby enhancing the denoising performance in both the simulation and experimental domains. Experimental results demonstrate that the proposed PDA-Net successfully enhances the quality of STM images, offering promising applications to enhance scientific discovery and accelerate experimental quantum material research.