?The cell is the basic unit of body structure and function composed of different organelles and the cytoplasmic matrix. The biological activities such as the characterization of its activity, functional expression, and morphological structure are illustrated by microscopies. With the development of optical technology, fluorescence super-resolution microscopies break through the diffraction limit of light and enable high-definition imaging of organelles included mitochondria, lysosomes, and ribosomes. It could provide scientific basis for revealing basic biomedical issues such as cell division, cell differentiation, cellular senescence, cellular apoptosis, and cellular communication. For example, tumor cells have the characteristics of vigorous growth and rapid proliferation, while have the risk of metastasis. The multi-drug resistance, toxicity and side effects lead to poor therapeutic effects during the process of anti-cancer drug discovery and development. With the innovation of super-resolution microscopy technologies, more subtle subcellular structure are realized to visualize tracking and position. It provides new insight for revealing the mechanism of material exchange and signal transduction under organelle interactions.

Photoacoustic imaging (PAI) is a non-invasive imaging technique that combines the principles of optical and ultrasound imaging to visualize internal biological structures at high spatial resolution. The main principle of PAI consists of the instantaneous thermal expansion of light absorbers caused by pulsed laser illumination and the detection of the resulting ultrasonic signals. The major characteristics of PAI, such as its sensitivity to optically absorptive targets (e.g., hemoglobin (Hb) and melanin), centimeters-deep imaging depth, and non-invasiveness, make it particularly effective in the diagnosis of skin diseases including cancers, inflammatory diseases, and vascular abnormalities.

The absorption and scattering by water and suspended particles severely disrupt the optical field information during the propagation of light. Due to the extremely high demand for the integrity of optical field information in end-to-end imaging technology, image detail loss is easily caused during underwater imaging. Correlated imaging technology reconstructs the object image by calculating the correlation of optical field intensity fluctuations, significantly reducing the demand for detailed optical field information. However, traditional correlated imaging algorithms require the acquisition of a large amount of data from the object. Compressive sensing and deep learning algorithms can construct higher-order functions to nonlinearly analyze the optical field intensity fluctuations, thereby significantly reducing the number of data collection times for the object. Yet, the imaging results heavily rely on the quality of one-dimensional light intensity information collection. Therefore, how to efficiently utilize multidimensional information of the object in a strong absorption and scattering underwater environment remains an urgent problem to be solved.