Zhao Zewei, Shen Binglin, Li Yanping, Wang Shiqi, Hu Rui, Qu Junle, Lu Yuan, Liu Liwei
Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and Ministry of Education, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Department of Dermatology, Shenzhen Nanshan People's Hospital and The 6th Affiliated Hospital of Shenzhen University Health Science Center, and Hua Zhong University of Science and Technology Union Shenzhen Hospital, China.
Biomed Opt Express. 2022 Dec 7;14(1):65-80. doi: 10.1364/BOE.476737. eCollection 2023 Jan 1.
Multiphoton microscopy is a formidable tool for the pathological analysis of tumors. The physical limitations of imaging systems and the low efficiencies inherent in nonlinear processes have prevented the simultaneous achievement of high imaging speed and high resolution. We demonstrate a self-alignment dual-attention-guided residual-in-residual generative adversarial network trained with various multiphoton images. The network enhances image contrast and spatial resolution, suppresses noise, and scanning fringe artifacts, and eliminates the mutual exclusion between field of view, image quality, and imaging speed. The network may be integrated into commercial microscopes for large-scale, high-resolution, and low photobleaching studies of tumor environments.
多光子显微镜是肿瘤病理分析的强大工具。成像系统的物理限制以及非线性过程固有的低效率阻碍了高成像速度和高分辨率的同时实现。我们展示了一种通过各种多光子图像训练的自对准双注意力引导的残差内残差生成对抗网络。该网络增强了图像对比度和空间分辨率，抑制了噪声和扫描条纹伪像，并消除了视野、图像质量和成像速度之间的相互排斥。该网络可集成到商业显微镜中，用于肿瘤环境的大规模、高分辨率和低光漂白研究。
