Self-supervised deep-learning two-photon microscopy

Artificial neural networks have shown great proficiency in transforming low-resolution microscopic images into high-resolution images. However, training data remains a challenge, as large-scale open-source databases of mi-croscopic images are rare, particularly 3D data. Moreover, the long training times and the need for expensive computational resources have become a burden to the research community. We introduced a deep-learning-based self-supervised volumetric imaging approach, which we termed "Self-Vision." The self-supervised approach re-quires no training data, apart from the input image itself. The lightweight network takes just minutes to train and has demonstrated resolution-enhancing power on par with or better than that of a number of recent microscopy -based models. Moreover, the high throughput power of the network enables large image inference with less post -processing, facilitating a large field-of-view (2.45 mm x 2.45 mm) using a home-built two-photon microscopy system. Self-Vision can recover images from fourfold undersampled inputs in the lateral and axial dimensions, dramatically reducing the acquisition time. Self-Vision facilitates the use of a deep neural network for 3D micros-copy imaging, easing the demanding process of image acquisition and network training for current resolution -enhancing networks. (c) 2022 Chinese Laser Press