[IPOL announce] new article: A Brief Evaluation of InSAR Phase Denoising and Coherence Estimation with Phi-Net

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2024/549/

Roland Akiki, Jérémy Anger, Carlo de Franchis, Gabriele Facciolo, 
Raphaël Grandin, and Jean-Michel Morel,
A Brief Evaluation of InSAR Phase Denoising and Coherence Estimation 
with Phi-Net,
Image Processing On Line, 14 (2024), pp. 205–216.
https://doi.org/10.5201/ipol.2024.549

Abstract
In this article, we examine the joint InSAR phase denoising and 
coherence estimation performance of the network known as Phi-Net [Sica 
et al., IEEE Transactions on Geoscience and Remote Sensing, 2021]. We 
briefly examine the method, network architecture, training data and 
strategy. Then, in the experimental section, we compare the network's 
performance against the simple boxcar uniform filter. We verify the 
observations made by the authors, in particular concerning the superior 
denoising performance and preservation of fine details in the coherence 
estimation. Our experiments also indicate that an end-to-end deep 
learning method might bring a small improvement to the patch-based 
approach adopted in Phi-Net.