Adjustment of laser scattering focus with machine learning

Abstract

The adjustment of the intensity of laser scattering focus with machine learning based on the back propagation (BP) neural network is proposed in this paper. Focus based on 'laser scattering' is more flexible than that based on a lens and imaging based on 'laser scattering focus' can break through the diffraction limit of lens imagingin order to obtain higher resolution. Therefore, knowing how to adjust the scattering focus is very important. In this paper, we used known data to train a BP network, and then used the trained BP network to realize the rapid and arbitrary adjustment of the intensity of the scattering focus. This method has potential application value in the field of 'laser scattering focus'. In addition, with the rapid development of artificial intelligence, machine learning is increasingly being used in various fields, including physics. We hope that machine learning can also be applied to 'laser scattering focus'. Therefore, in this paper, we conducted some preliminary investigations, which could provide reference for further research.

Memory effect evaluation based on transmission matrix calculation*

“…In the range of memory effect, each time the target point moves by two pixels. Thus, the 8 focal points are adjacent on the CCD screen, where the central coordinates of the focal points are [20,40], [20,42], [20,44], [20,46], [20,48], [20,50], [20,52], and [20,54], respectively. We integrate the intensity of 3 × 3 pixels around the central points to save in a computer as the feedback signal to control the spatial light modulator.…”

“…We integrate the intensity of 3 × 3 pixels around the central points to save in a computer as the feedback signal to control the spatial light modulator. Figures 7(a [20,68], [20,70], [20,72], and [20,74]. We integrate the intensity of 3 × 3 pixels around the central points to save in a computer as the feedback signal to control the spatial light modulator.…”

“…[16] The knowledge of optical memory effect and the TM approach to focusing light through turbid media have been widely studied and can be applied to many research fields. [9,[17][18][19][20] However, little work has studied on the relationship between the optimal phase masks of two adjacent focal points. In this paper, we predict that there exists a strong correlation among the optimized phase distributions of adjacent focal points in focusing through scattering media.…”

Phase-shift interferometry measured transmission matrix of turbid medium: Three-step phase-shifting interference better than four-step one

“…[19] The rise of this technology is inseparable from spatial light modulators (SLM) that can control light on a micrometer scale. [20] Such a control can be achieved through wavefront shaping technologies [1] such as iterative feedback algorithm, [1,3] intelligent optimization algorithm, [21] neural network algorithm, [22] and transmission matrix (TM) method. [23,24] In 2010, Popoff et al used an SLM combined with full-field phase-shifting interferometry to measure monochromatic light TM (a measure of transfer function) [23] in turbid media.…”

A scanning-free wide-field single-fiber endoscopic image retrieval method based on optical transmission matrix

“…In fact, many methods have been proposed recently as for the endoscopic imaging applications using MMF [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. These methods can be classified into two main types.…”