Date

2022-12

Author

Kürekci, Şahin

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When a light beam travels through a highly scattering medium, two-dimensional random intensity distributions (speckle patterns) are formed due to the complex scattering within the medium. Although they contain valuable information about the input signal and the characteristics of the propagation medium, the speckle patterns are difficult to unscramble, which makes imaging through scattering media an extremely challenging task. Multimode fibers behave similarly to scattering media since they scramble the input information through modal dispersion and create speckle patterns at the distal end. Because multimode fibers are compact and low-cost structures with the ability to transmit large amounts of data simultaneously for long distances, decoding the speckle patterns formed by a multimode fiber and reconstructing the input information has great implications in a wide range of applications, including fiber optic communication, sensor technology, optical imaging, and invasive biomedical applications such as endoscopy. In this thesis, we decode the speckle patterns and reconstruct the input information on the proximal end of a multimode fiber in three different scenarios. Our choice of input signals consists of numbers encoded as binary digits, handwritten letters, and optical frequencies. We train a deep learning model to classify and reconstruct the handwritten letters, while for the rest of the cases, we construct a transmission matrix between the input signals and the output speckle patterns, and solve the inverse propagation equation algebraically. In all cases, the relation between a speckle pattern and the corresponding input signal is learned with low error rates; thus, the signals are classified and reconstructed successfully using the speckle patterns they created. Classifying digits, letters, or images with speckle information aims to build useful systems in optical imaging, communication, and cryptography, while the classification of optical frequencies paves the way for building novel spectrometers. In addition to replicating the currently existing compact, low-budget, and high-resolution multimode fiber spectrometer, we also build a single-pixel fiber spectrometer in order to increase the compactness on the detection side and expand the application areas of the system. The single-pixel spectrometer we offer is based on the integrated intensity measurements of a fixed target region, where the light is focused by shaping the wavefront with a spatial light modulator. Spatial light modulators and wavefront shaping techniques are also utilized in other classification tasks in this thesis to generate the desired input signals.

Subject Keywords

Fiber optics, Multimode fibers, Speckle pattern, Propagation, Imaging, Classification, Reconstruction, Wavefront shaping, Spatial light modulator, Cryptography, Encryption, Deep learning, Neural networks, Spectrometer, Single-pixel, High resolution

URI

https://hdl.handle.net/11511/101287

Collections

Graduate School of Natural and Applied Sciences, Thesis