*Proceeding Paper* **Roles of Deep Learning in Optical Imaging †**

**Vineela Chandra Dodda <sup>1</sup> and Inbarasan Muniraj 1,2,\***


**Abstract:** Imaging-based problem-solving approaches are an exemplary way of handling problems in various scientific applications. With an increased demand for automation, artificial intelligence techniques have shown exponential growth in recent years. In this context, deep-learning-based "learned" solutions have been widely adopted in many applications and are thus slowly becoming an inevitable alternative tool. It is known that in contrast to the conventional "physics-based" approach, deep learning models are a "data-driven" approach, where the outcomes are based on data analysis and interpretation. Thus, deep learning approaches have been applied in several (optical and computational) imaging-based scientific problems such as denoising, phase retrieval, hologram reconstruction, and histopathology, to name a few. In this work, we present two deep-learning networks for 3D image denoising and off-focus voxel removal.

**Keywords:** optical 3D imaging; unsupervised denoising; off-focus removal; integral imaging
