Research Projects

The proposed research effort aims to demonstrate microscope prototype for phase (3D) imaging of small objects such as particulate matter, contaminants in water, and cells with or without the use of staining or fluorescent labeling. We intend to present a quantitative phase (3D) imaging system with in-line geometry that is less expensive or on par with commercial brightfield (2D) microscopes in terms of cost. In addition, compared to common 2D microscopes, phase (3D) imaging systems have more information and additional parameters. It follows that accuracy should increase naturally. The proposed task includes both the development of robust algorithms and hardware. For precise and effective algorithm development, which is a challenging task, cutting-edge concepts like sparsity, machine learning, and/or deep learning will be used, which have demonstrated promising outcomes in a number of fields. Some of the imaging applications of interest will be demonstrated and deep learning/machine learning ideas can also be utilized for automatic quantitative parameters measurement. Building point-of-care devices for imaging small cell like things, such as blood cells and particle tracking in water and air, is suited for in-line holographic setup because it is simpler, compact, and less expensive than off-axis geometry setup. In the Kashmir region, cancer and the presence of contaminants in the water are major issues. It will be quite valuable to have access to such a proposed tool for impurity testing, blood screening, etc. The thorough investigation could potentially result in the creation of a point-of-care quantitative tool based on deep learning and machine learning principles.