Deep learning-based automated defect detection in digital holographic microscopy

Abstract

The article introduces a defect identification method using digital holographic microscopy and deep learning. It utilizes wrapped phase from holograms to generate binary defect maps trained for high noise levels. Experimental results validate its efficacy.