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|Title:||Ultrahigh-resolution spectral CCD imaging based on machine learning||Authors:||Xie, Hui||Keywords:||Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics||Issue Date:||2021||Publisher:||Nanyang Technological University||Source:||Xie, H. (2021). Ultrahigh-resolution spectral CCD imaging based on machine learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153855||Abstract:||Spectral imaging has played a significant role in many applications, including earth remote sensing, military, and biomedical solutions. The request for higher spectral resolution has been in demand, especially for laser emission-based detection and imaging. Various systems and configurations have been developed over the past decades. However, state-of-art cameras relies heavily on optical filters or a bulky spectrometer system to achieve high spectral resolution. As such, the ability to obtain wavelength information from normal CCD images remains a key challenge. Herein, we developed a smart algorithm to identify emission wavelengths captured by a conventional CCD camera. A total of 6000 CCD images with precise laser emissions from 400 nm to 670 nm were used to train the model. Red, green, blue channels and its corresponding grey value were extracted as the pixel feature. Three advanced modeling methods were employed based on the Python library scikit-learn, namely, decision tree, k-nearest neighbor (KNN) and gradient boosting regression (GBR) model. Under the same wavelength and luminance condition, the reranked feature between the tenth and ninetieth percentile remained unchanged, otherwise modified to the median. Based on the model developed, we applied the algorithm to a multi-wavelength image captured from CCD for real imaging applications of microparticles. Our results showed the possibility of spectral mapping with a high spectral resolution of 0.98 nm while maintaining pixel-sized spatial resolution. The proposed method could be applied to a wide range of imaging applications.||URI:||https://hdl.handle.net/10356/153855||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
Updated on May 19, 2022
Updated on May 19, 2022
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