Speckle Noise Suppression in Digital Images Utilizing Deep Refinement Network

  • Mohamed AbdelNasser Aswan University
  • Ehab Alaa Saleh Aswan University
  • Mostafa I. Soliman Egypt-Japan University of Science and Technology
Keywords: Speckle Noise, Image Filtering, Image Despecking, Denoising, Image Enhancement, Deep Learning

Abstract

This paper proposes a deep learning model for speckle noise suppression in digital images. The model consists of two interconnected networks: the first network focuses on the initial suppression of speckle noise. The second network refines these features, capturing more complex patterns, and preserving the texture details of the input images. The performance of the proposed model is evaluated with different backbones for the two networks: ResNet-18, ResNet-50, and SENet-154. Experimental results on two datasets, the Boss steganography, and COVIDx CXR-3, demonstrate that the proposed method yields competitive despeckling results. The proposed model with the SENet-154 encoder achieves PSNR and SNR values higher than 37 dB with the two datasets and outperforms other state-of-the-art methods (Pixel2Pixel, DiscoGAN, and BicycleGAN).

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Published
2024-07-15
How to Cite
[1]
AbdelNasser, M., Saleh, E. and Soliman, M. 2024. Speckle Noise Suppression in Digital Images Utilizing Deep Refinement Network. MENDEL. 30, 1 (Jul. 2024), 15-22. DOI:https://doi.org/10.13164/mendel.2024.1.015.
Section
Research articles