國立虎尾科技大學 |

Deep Learning-Based Image Reconstruction From Multimode Fiber : = Comparative Evaluation of Various Approaches.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Deep Learning-Based Image Reconstruction From Multimode Fiber :/
其他題名:	Comparative Evaluation of Various Approaches.
作者:	Mohammadzadeh, Mohammad.
面頁冊數:	1 online resource (130 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798383058169

Deep Learning-Based Image Reconstruction From Multimode Fiber : = Comparative Evaluation of Various Approaches.
Mohammadzadeh, Mohammad.

Deep Learning-Based Image Reconstruction From Multimode Fiber :Comparative Evaluation of Various Approaches. - 1 online resource (130 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Southern Illinois University at Carbondale, 2024.

Includes bibliographical references

This dissertation presents three distinct methodologies aimed at exploring pivotal aspects within the domain of fiber optics and piezoelectric materials. The first approach offers a comprehensive exploration of three pivotal aspects within the realm of fiber optics and piezoelectric materials. The study delves into the influence of voltage variation on piezoelectric displacement, examines the effects of bending multimode fiber (MMF) on data transmission, and scrutinizes the performance of an Autoencoder in MMF image reconstruction with and without additional noise. To assess the impact of voltage variation on piezoelectric displacement, experiments were conducted by applying varying voltages to a piezoelectric material, meticulously measuring its radial displacement. The results revealed a notable increase in displacement with higher voltage, presenting implications for fiber stability and overall performance. Additionally, the investigation into the effects of bending MMF on data transmission highlighted that the bending process causes the fiber to become leaky and radiate power radially, potentially affecting data transmission. This crucial insight emphasizes the necessity for further research to optimize data transmission in practical fiber systems. Furthermore, the performance of an Autoencoder model was evaluated using a dataset of MMF images, in diverse scenarios. The Autoencoder exhibited impressive accuracy in reconstructing MMF images with high fidelity. The results underscore the significance of ongoing research in these domains, propelling advancements in fiber optic technology.The second approach of this dissertation entails a comparative investigation involving three distinct neural network models to assess their efficacy in improving image quality within optical transmissions through multimode fibers, with a specific focus on mitigating speckle patterns. Our proposed methodology integrates multimode fibers with a piezoelectric source, deliberately introducing noise into transmitted images to evaluate their performance using an autoencoder neural network. The autoencoder, trained on a dataset augmented with noise and speckle patterns, adeptly eliminates noise and reconstructs images with enhanced fidelity. Comparative analyses conducted with alternative neural network architectures, namely a single hidden layer (SHL) model and a U-Net architecture, reveal that while U-Net demonstrates superior performance in terms of image reconstruction fidelity, the autoencoder exhibits notable advantages in training efficiency. Notably, the autoencoder achieves saturation SSIM in 450 epochs and 24 minutes, surpassing the training durations of both U-Net (210 epochs, 1 hour) and SHL (160 epochs, 3 hours and 25 minutes) models. Impressively, the autoencoder's training time per epoch is six times faster than U-Net and fourteen times faster than SHL. The experimental setup involves the application of varying voltages via a piezoelectric source to induce noise, facilitating adaptation to real-world conditions. Furthermore, the study not only demonstrates the efficacy of the proposed methodology but also conducts comparative analyses with prior works, revealing significant improvements. Compared to Li et al.'s study, our methodology, particularly when utilizing the pre-trained autoencoder, demonstrates an average improvement of 15% for SSIM and 9% for PSNR in the worst-case scenario. Additionally, when compared to Lai et al.'s study employing a generative adversarial network for image reconstruction, our methodology achieves slightly superior SSIM outcomes in certain scenarios, reaching 96%. The versatility of the proposed method is underscored by its consistent performance across varying voltage scenarios, showcasing its potential applications in medical procedures and industrial inspections. This research not only presents a comprehensive and innovative approach to addressing challenges in optical image reconstruction but also signifies significant advancements compared to prior works.The final approach of this study entails employing Hermit Gaussian Functions with varying orders as activation functions within a U-Net model architecture, aiming to evaluate its effectiveness in image reconstruction. The performance of the model is rigorously assessed across five distinct voltage scenarios, and a supplementary evaluation is conducted with digit 5 excluded from the training set to gauge its generalization capability. The outcomes offer promising insights into the efficacy of the proposed methodologies, showcasing significant advancements in optical image reconstruction. Particularly noteworthy is the robust accuracy demonstrated by the higher orders of the Hermit Gaussian Function in reconstructing MMF images, even amidst the presence of noise introduced by the voltage source. However, a decline in accuracy is noted in the presence of voltage-induced noise, underscoring the imperative need for further research to bolster the model's resilience in real-world scenarios, especially in comparison to the utilization of the Rectified Linear Unit (ReLU) function.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383058169Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

554714
Electronic books.

Deep Learning-Based Image Reconstruction From Multimode Fiber : = Comparative Evaluation of Various Approaches.
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Additionally, the investigation into the effects of bending MMF on data transmission highlighted that the bending process causes the fiber to become leaky and radiate power radially, potentially affecting data transmission. This crucial insight emphasizes the necessity for further research to optimize data transmission in practical fiber systems. Furthermore, the performance of an Autoencoder model was evaluated using a dataset of MMF images, in diverse scenarios. The Autoencoder exhibited impressive accuracy in reconstructing MMF images with high fidelity. The results underscore the significance of ongoing research in these domains, propelling advancements in fiber optic technology.The second approach of this dissertation entails a comparative investigation involving three distinct neural network models to assess their efficacy in improving image quality within optical transmissions through multimode fibers, with a specific focus on mitigating speckle patterns. Our proposed methodology integrates multimode fibers with a piezoelectric source, deliberately introducing noise into transmitted images to evaluate their performance using an autoencoder neural network. The autoencoder, trained on a dataset augmented with noise and speckle patterns, adeptly eliminates noise and reconstructs images with enhanced fidelity. Comparative analyses conducted with alternative neural network architectures, namely a single hidden layer (SHL) model and a U-Net architecture, reveal that while U-Net demonstrates superior performance in terms of image reconstruction fidelity, the autoencoder exhibits notable advantages in training efficiency. Notably, the autoencoder achieves saturation SSIM in 450 epochs and 24 minutes, surpassing the training durations of both U-Net (210 epochs, 1 hour) and SHL (160 epochs, 3 hours and 25 minutes) models. Impressively, the autoencoder's training time per epoch is six times faster than U-Net and fourteen times faster than SHL. The experimental setup involves the application of varying voltages via a piezoelectric source to induce noise, facilitating adaptation to real-world conditions. Furthermore, the study not only demonstrates the efficacy of the proposed methodology but also conducts comparative analyses with prior works, revealing significant improvements. Compared to Li et al.'s study, our methodology, particularly when utilizing the pre-trained autoencoder, demonstrates an average improvement of 15% for SSIM and 9% for PSNR in the worst-case scenario. Additionally, when compared to Lai et al.'s study employing a generative adversarial network for image reconstruction, our methodology achieves slightly superior SSIM outcomes in certain scenarios, reaching 96%. The versatility of the proposed method is underscored by its consistent performance across varying voltage scenarios, showcasing its potential applications in medical procedures and industrial inspections. This research not only presents a comprehensive and innovative approach to addressing challenges in optical image reconstruction but also signifies significant advancements compared to prior works.The final approach of this study entails employing Hermit Gaussian Functions with varying orders as activation functions within a U-Net model architecture, aiming to evaluate its effectiveness in image reconstruction. The performance of the model is rigorously assessed across five distinct voltage scenarios, and a supplementary evaluation is conducted with digit 5 excluded from the training set to gauge its generalization capability. The outcomes offer promising insights into the efficacy of the proposed methodologies, showcasing significant advancements in optical image reconstruction. Particularly noteworthy is the robust accuracy demonstrated by the higher orders of the Hermit Gaussian Function in reconstructing MMF images, even amidst the presence of noise introduced by the voltage source. However, a decline in accuracy is noted in the presence of voltage-induced noise, underscoring the imperative need for further research to bolster the model's resilience in real-world scenarios, especially in comparison to the utilization of the Rectified Linear Unit (ReLU) function.
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