Noise Suppression on In-line Holograms with a Hybrid Method Using U-Net and Transformer Architectures U-Net ve Transformer Mimarilerini Kullanan Hibrit Bir Y ntem ile Eksen- st Hologramlarda G r lt Bastirimi

SÜSLEYİCİ B., Gokgoz F., Ozturk A., ESMER G. B.

33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025, İstanbul, Turkey, 25 - 28 June 2025, (Full Text) identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu66497.2025.11111867
  • City: İstanbul
  • Country: Turkey
  • Keywords: deep learning, denoising, digital holography, Transformer, U-Net
  • Marmara University Affiliated: Yes

Abstract

Digital holography enables capturing three-dimensional images from imaging methods but is often hindered by significant noise and interference artifacts. In this work, we propose a deep learning-based denoising method for inline holograms that leverages a hybrid CNN-Transformer U-Net architecture with multi-step Transformer refinement. Our approach integrates convolutional neural networks for local feature extraction with Transformer-based self-attention for modeling global context. In the experimental results, the performance of the proposed method was evaluated using PSNR, SSIM, and MSE metrics, and promising results were obtained.