30 March 2021 Interferometric phase image denoising method via residual learning
Boyu Liu, Lingda Wu, Hongxing Hao, Junshuo Dong
Author Affiliations +
Abstract

The denoising of interferometric phase images attracts many researchers. Natural image denoising algorithms based on neural networks are often proposed in the development of deep learning methods. A neural network, In-CNN, is derived from an advanced natural image denoising network and proposed for interferometric phase image denoising. Preactivation and residual learning methods are combined and applied to the function of the neural network nodes. Considering the particularity of the interferometric phase image, we propose a neural network based on the rational application of the preactivation mode and feedforward mapping, which is different from previous natural image denoising networks. We also construct a training set for an interferometric phase image denoising neural network. We experimentally verify that our model performs better than state-of-the-art interferometric phase image denoising methods based on sparse representation and advanced natural image denoising networks. We discuss the complexity of traditional interferometric phase image denoising algorithms to demonstrate the efficiency of the proposed method.

© 2021 SPIE and IS&T 1017-9909/2021/$28.00© 2021 SPIE and IS&T
Boyu Liu, Lingda Wu, Hongxing Hao, and Junshuo Dong "Interferometric phase image denoising method via residual learning," Journal of Electronic Imaging 30(2), 023013 (30 March 2021). https://doi.org/10.1117/1.JEI.30.2.023013
Received: 28 September 2020; Accepted: 16 March 2021; Published: 30 March 2021
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CITATIONS
Cited by 4 scholarly publications.
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KEYWORDS
Denoising

Image denoising

Interferometry

Phase interferometry

Error analysis

Interferometric synthetic aperture radar

Neural networks

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