Browsing by Author "Md. Maidul Islam"

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    Enhancing Computer Generated Hologram Quality via Deep Learning Based On U-Net Architecture
    (2024-05-30) Md. Maidul Islam
    This thesis investigates the advancement of computer-generated hologram (CGH) quality using deep learning methodologies, with a focus on the U-Net architecture. Employing the DIV2k dataset as the foundation, the study proposes a novel approach integrating a specialized 3U configuration within the U-Net framework. This configuration comprises meticulously designed U-shaped modules tailored to extract intricate details and subtle nuances inherent in holographic imagery. Evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and reconstruction time are employed to assess the model's performance. Comparative analysis against established models, namely Holonet and Holoencoder, underscores the superior efficacy of the proposed method in faithfully reproducing holographic images. The findings reveal substantial enhancements in hologram quality, characterized by elevated PSNR and SSIM values, alongside notable gains in computational efficiency manifested through reduced reconstruction times. This research contributes significantly to the progression of holographic imaging technology, offering promising avenues for future exploration and innovation in the realm of computer-generated holography.