2020/01/24 by Emrah Bostan, Bostan, Emrah, Reinhard Heckel +7 · 2 citations
Computer Science · Engineering · Physics and Astronomy · #Digital Holography and Microscopy #FOS: Electrical engineering #FOS: Physical sciences #Image Processing Techniques and Applications #Image and Video Processing (eess.IV) #Optical measurement and interference techniques #Optics (physics.optics) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2001.09803
openalex publication_date 2020/01/24 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Deep neural networks have emerged as effective tools for computational\nimaging including quantitative phase microscopy of transparent samples. To\nreconstruct phase from intensity, current approaches rely on supervised\nlearning with training examples; consequently, their performance is sensitive\nto a match of training and imaging settings. Here we propose a new approach to\nphase microscopy by using an untrained deep neural network for measurement\nformation, encapsulating the image prior and imaging physics. Our approach does\nnot require any training data and simultaneously reconstructs the sought phase\nand pupil-plane aberrations by fitting the weights of the network to the\ncaptured images. To demonstrate experimentally, we reconstruct quantitative\nphase from through-focus images blindly (i.e. no explicit knowledge of the\naberrations).\n