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Digital pathology is being transformed by artificial intelligence (AI)-based pathological diagnosis. One major challenge for correct AI diagnoses is to ensure the focus quality of captured images. Here, we propose a deep learning-based single-shot autofocus method for microscopy. We use a modified MobileNetV3, a lightweight network, to predict the defocus distance with a single-shot microscopy image acquired at an arbitrary image plane without secondary camera or additional optics. The defocus prediction takes only 9 ms with a focusing error of only ∼1/15 depth of field. We also provide implementation examples for the augmented reality microscope and the whole slide imaging (WSI) system. Our proposed technique can perform real-time and accurate autofocus which will not only support pathologists in their daily work, but also provide potential applications in the life sciences, material research, and industrial automatic detection.

Fluorescence imaging through a turbid layer holds great promise for various biophotonics applications. Conventional wavefront shaping techniques aim to create and scan a focus spot through the turbid layer. Finding the correct input wavefront without direct access to the target plane remains a critical challenge. In this paper, we explore a new strategy for imaging through turbid layer with a large field of view. In our setup, a fluorescence sample is sandwiched between two turbid layers. Instead of generating one focus spot via wavefront shaping, we use an unshaped beam to illuminate the turbid layer and generate an unknown speckle pattern at the target plane over a wide field of view. By tilting the input wavefront, we raster scan the unknown speckle pattern via the memory effect and capture the corresponding low-resolution fluorescence images through the turbid layer. Different from the wavefront-shaping-based single-spot scanning, the proposed approach employs many spots (i.e., speckles) in parallel for extending the field of view. Based on all captured images, we jointly recover the fluorescence object, the unknown optical transfer function of the turbid layer, the translated step size, and the unknown speckle pattern. Without direct access to the object plane or knowledge of the turbid layer, we demonstrate a 13-fold resolution gain through the turbid layer using the reported strategy. We also demonstrate the use of this technique to improve the resolution of a low numerical aperture objective lens allowing to obtain both large field of view and high resolution at the same time. The reported method provides insight for developing new fluorescence imaging platforms and may find applications in deep-tissue imaging.