Convolutional denoising of underwater laser line scan images via transfer learning

Christopher Campbell; Fauzia Ahmad

doi:10.1117/12.2618776

31 May 2022 Convolutional denoising of underwater laser line scan images via transfer learning

Christopher Campbell, Fauzia Ahmad

Author Affiliations +

Proceedings Volume 12097, Big Data IV: Learning, Analytics, and Applications; 120970F (2022) https://doi.org/10.1117/12.2618776
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

In this paper, we consider the problem of Poisson noise suppression in underwater laser line scan (LLS) imagery for image quality enhancement. We investigate two different denoising neural network architectures, one based on a convolutional autoencoder (CAE) and a commonly used convolutional neural network (CNN) based denoiser. Due to the relative abundance of camera images over underwater LLS imagery, we employ transfer learning with camera images as training data for the CAE and CNN architectures. Poisson noise is introduced in these training images at varying levels to mimic a noise-dominated LLS system. Images from an underwater scene under different turbidity conditions are used for testing. Using a composite loss function consisting of l1 and l2 norms, we demonstrate the noise suppression capabilities of both architectures in underwater LLS images via transfer learning. The results show that the CAE outperforms the CNN denoiser qualitatively and quantitatively in terms of the contrast ratio and contrast signal-to-noise ratio.

Citation Download Citation

Christopher Campbell and Fauzia Ahmad "Convolutional denoising of underwater laser line scan images via transfer learning", Proc. SPIE 12097, Big Data IV: Learning, Analytics, and Applications, 120970F (31 May 2022); https://doi.org/10.1117/12.2618776

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