Two phase imaging methods with transport of intensity equation (TIE) under a low signal to noise ratio (SNR) are introduced. One is a TIE phase imaging with deep learning. It is useful for parallel TIE using a diffractive optics to produce defocus images simultaneously. The defocus images are obtained by an optical convolution integral by the calculated blurred point spread functions. However the point spread functions are different from the ideals due to the limited extent and/or limited resolution of the diffractive optics. This means that an SNR of the through-focus images is low. Therefore, deep learning compensates the error. Another is transport-of- intensity computational ghost imaging (TI-CGI). It is a combination of TIE and a computational ghost imaging (CGI). It is useful for noninvasive imaging for the biomedical field because most cells are photo-sensitive and often suffer from phototoxicity. However, CGI can obtain only amplitude information. In the biomedical field, a phase information is important to know the physical parameters. To achieve, under weak illumination, it is difficult to obtain through-focus images with high SNRs. Therefore, combination of TIE and CGI is useful.
Quantitative phase imaging is widely studied for such as bio-imaging and industrial inspection. Quantitative phase imaging is divided into interferometric approach and non-interferometric intensity-based one. Interferometry often uses object and reference arms, rendering an optical setup complicated. The transport of intensity equation has been used for non-interferometric quantitative phase imaging. It allows to retrieve a phase distribution from through-focus series of an object. To obtain through-focus series, mechanical scanning of the object or a camera is required. This is not suitable for the quantitative phase imaging of dynamic phenomena. In this presentation, some of our proposed scan-less methods are presented. Numerical and/or experimental results are also shown.
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