Application of Machine Learning Methods for Identifying Wave Aberrations from Combined Intensity Patterns Generated Using a Multi-Order Diffractive Spatial Filter
Abstract
:1. Introduction
2. Materials and Methods
2.1. Theoretical Foundations
2.2. Dataset
2.3. CNN Architecture
2.4. Optical Scheme
3. Results
3.1. Modeling Dataset
3.2. CNN Training
3.3. Experimental Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Layer Type | Kernel Size | Strides | Output Size |
---|---|---|---|
Input | - | - | (256, 256, 3) |
Conv2D | 3 × 3 | 2 × 2 | 149 × 149 × 32 |
Conv2D | 3 × 3 | 1 × 1 | 147 × 147 × 64 |
SeparableConv2D | 3 × 3 | 1 × 1 | 147 × 147 × 128 |
SeparableConv2D | 3 × 3 | 1 × 1 | 147 × 147 × 128 |
MaxPooling2D | 3 × 3 | 2 × 2 | 73 × 73 × 128 |
SeparableConv2D | 3 × 3 | 1 × 1 | 73 × 73 × 256 |
SeparableConv2D | 3 × 3 | 1 × 1 | 73 × 73 × 256 |
MaxPooling2D | 3 × 3 | 2 × 2 | 37 × 37 × 256 |
SeparableConv2D | 3 × 3 | 1 × 1 | 37 × 37 × 728 |
SeparableConv2D | 3 × 3 | 1 × 1 | 37 × 37 × 728 |
MaxPooling2D | 3 × 3 | 2 × 2 | 19 × 19 × 728 |
Layer Type | Kernel Size | Strides | Output Size |
---|---|---|---|
SeparableConv2D | 3 × 3 | 1 × 1 | 19 × 19 × 728 |
SeparableConv2D | 3 × 3 | 1 × 1 | 19 × 19 × 728 |
SeparableConv2D | 3 × 3 | 1 × 1 | 19 × 19 × 728 |
Layer Type | Kernel Size | Strides | Output Size |
---|---|---|---|
SeparableConv2D | 3 × 3 | 1 × 1 | 19 × 19 × 728 |
SeparableConv2D | 3 × 3 | 1 × 1 | 19 × 19 × 1024 |
MaxPooling2D | 3 × 3 | 2 × 2 | 10 × 10 × 1024 |
SeparableConv2D | 3 × 3 | 1 × 1 | 10 × 10 × 1536 |
SeparableConv2D | 3 × 3 | 1 × 1 | 10 × 10 × 2048 |
GlobalAveragePooling | - | - | 2048 |
Dropout | - | - | 2048 |
Dense (linear) | - | - | 8 (Classes) |
c11 = 0.20, c20 = 0.30 | c11 = 0.20, c22 = 0.30 | c22 = 0.15, c33 = 0.10 |
c42 = 0.20, c33 = 0.15 | c44 = 0.20, c42 = 0.25 | c44 = 0.25, c22 = 0.20 |
c11 = 0.20, c20 = 0.30 | c11 = 0.20, c22 = 0.30 | c22 = 0.15, c33 = 0.10 |
c42 = 0.20, c33 = 0.15 | c44 = 0.20, c42 = 0.25 | c44 = 0.25, c22 = 0.20 |
= −0.5 | = −0.3 | = −0.2 |
---|---|---|
= 0.2 | = 0.3 | = 0.5 |
---|---|---|
(n, m) | ||
---|---|---|
(2, 0) = 0.20 | ||
(3, 3) = 0.25 | ||
(3, 1) = 0.45 | ||
(4, 4) = 0.15 | ||
(4, 2) = 0.15 |
c11 = 0.20, c20 = 0.30 | c11 = 0.20, c22 = 0.30 | c22 = 0.15, c33 = 0.10 |
c42 = 0.20, c33 = 0.15 | c44 = 0.20, c42 = 0.25 | c44 = 0.25, c22 = 0.20 |
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Khorin, P.A.; Dzyuba, A.P.; Chernykh, A.V.; Butt, M.A.; Khonina, S.N. Application of Machine Learning Methods for Identifying Wave Aberrations from Combined Intensity Patterns Generated Using a Multi-Order Diffractive Spatial Filter. Technologies 2025, 13, 212. https://doi.org/10.3390/technologies13060212
Khorin PA, Dzyuba AP, Chernykh AV, Butt MA, Khonina SN. Application of Machine Learning Methods for Identifying Wave Aberrations from Combined Intensity Patterns Generated Using a Multi-Order Diffractive Spatial Filter. Technologies. 2025; 13(6):212. https://doi.org/10.3390/technologies13060212
Chicago/Turabian StyleKhorin, Paval. A., Aleksey P. Dzyuba, Aleksey V. Chernykh, Muhammad A. Butt, and Svetlana N. Khonina. 2025. "Application of Machine Learning Methods for Identifying Wave Aberrations from Combined Intensity Patterns Generated Using a Multi-Order Diffractive Spatial Filter" Technologies 13, no. 6: 212. https://doi.org/10.3390/technologies13060212
APA StyleKhorin, P. A., Dzyuba, A. P., Chernykh, A. V., Butt, M. A., & Khonina, S. N. (2025). Application of Machine Learning Methods for Identifying Wave Aberrations from Combined Intensity Patterns Generated Using a Multi-Order Diffractive Spatial Filter. Technologies, 13(6), 212. https://doi.org/10.3390/technologies13060212