Abstract: Keywords: Low coherence interferometry; Optical coherence tomography, post signal processing; axial resolution; Continuous wavelet transforms; Wigner-Ville distributions; Fruit tissue imaging; Speckle signal analysis; Artificial fruit ripening Optical Coherence Tomography (OCT) has emerged as a popular non-invasive portable imaging modality capable of providing depth-resolved visualization of microstructures in scattering media. In spectral domain OCT (SDOCT), the reconstruction of depth profiles (A-scans) is conventionally performed using the Fourier Transform (FT) of the measured spectral interferogram. Although FT-based reconstruction is computationally efficient, it is limited by finite depth sampling due to the inefficient utilization of detector resolution imposed by the complex conjugate ambiguity (CCA) artifact. As a result, only half of the available detector resolution effectively contributes to the usable imaging depth, thereby restricting axial resolution and limiting the ability to resolve closely spaced structures. With FFT based image reconstruction, utilizing the full detector resolution to reconstruct A-scan typically requires increasing the number of detector pixels. This directly increases the system cost and hardware footprint. To address this limitation, computational approaches have been explored to enhance resolution without modifying the hardware. This thesis addresses the limitation by exploring time–frequency techniques as an alternative computational framework for post-signal processing in SDOCT. In particular, the Continuous Wavelet Transform and the Wigner–Ville Distribution (WVD) are investigated. Among the methods studied, the smoothed pseudo WVD based image reconstruction framework demonstrated two-fold improvement (without increasing the number of pixels in the detector) in axial resolution and contrast to noise ratio (CNR) when compared to FFT method. Exploiting the properties of WVD, it is possible to reconstruct A-scans using the full detector resolution (N pixels), in contrast to the conventional FT-based approach, which utilizes only N/2 pixels due to CCA. Furthermore, generating depth information from localized spectral information of the interferometric signals improved the separation of closely spaced reflectors, which further led to the improved CNR in the reconstructed OCT images. A fiber-based spectral domain OCT system operating at a central wavelength of 1550 nm was designed and developed to experimentally validate the proposed methods. With the improved image reconstruction framework and CNR, the applicability of OCT was further explored in the domain of agriculture, particularly for fruit health monitoring. OCT offers unique advantages such as non-invasive, depth-resolved imaging, making it suitable for detecting internal structural changes that are not visible externally. This is especially relevant in fruits, where internal bruising or microstructural changes due to different ripening methods adopted may not be reflected in external appearance. In this work, two climacteric fruits, Mango and Banana, which are commonly exposed to harmful chemicals like calcium carbide to hasten ripening for market demand and availability in India, were investigated. For the present study, two ripening techniques approved by the Food Safety and Standards Authority of India were adopted. One is natural ripening using paddy straw and the other one is artificial ripening using ethylene gas. Ethylene-based ripening was selected as the artificial method, as it accelerates the ripening process in a manner similar to calcium carbide, while being a safer alternative and believed to induce similar effects on internal microstructures, albeit at a different rate. The sub-peel microstructural changes were observed using OCT imaging. Mango samples revealed noticeable differences in the thickness of the hypodermis layer and the patterns of cellular disintegration in the outer mesocarp regions between natural and ethylene-induced ripening. In the case of banana, speckle signal analysis was performed to quantitatively characterize tissue changes during ripening. Statistical analysis of the observed speckle patterns using the Burr distribution provided a biomarker to identify the ripening technique within 5 days of exposure to ethylene gas. The collected OCT image data base and the methods adopted for analyzing OCT data further establish the potential of combining machine learning algorithms to make OCT a non-invasive optical technique for fruit health assessment.

Event Details
Title: Computational techniques for enhanced axial resolution in spectral domain OCT and its application in fruit tissue imaging (PhD Viva Voce)
Date: July 13, 2026 at 09:00 AM
Venue: Google Meet (https://meet.google.com/axy-xmqv-jhw)
Speaker: Mr. Naveen Kumar P (EE19D015)
Guide: Dr. Shanti Bhattacharya
Type: PHD seminar

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