Speckle noise elimination of phase images using convolution neural networks

Yaghoobi, Mohammad; Jafarfard, Mohammad Rezaa; Zare, Babak

Speckle noise elimination of phase images using convolution neural networks

Authors

Mohammad Yaghoobi

Mohammad Rezaa Jafarfard

Babak Zare

Abstract

Phase images obtained from holograms and interference patterns in quantitative phase microscopy often suffer from distortions, phase wrapping, and phase noise. Despite extensive efforts over the past decades and the development of various denoising methods, these challenges have not yet been completely resolved. Classical noise reduction filters typically damage image details, reduce overall quality, and weaken boundary and edge detection, thereby eliminating useful image information. A promising modern approach involves the use of machine learning algorithms. Results from image quality assessment metrics show that convolutional neural networks outperform conventional phase image denoising methods. In this approach, training data are fed into the network, and after several training stages, the network acquires the ability to reduce noise in phase images with high accuracy.

Keywords

Phase microscopy

speckle Noise

Machine Learning

Convolution Neural Networks

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