# Optical Fringe Patterns Filtering Based on Multi-Stage Convolution Neural Network

## TL;DR

This paper introduces a multi-stage convolutional neural network designed to effectively denoise optical fringe patterns contaminated by speckle noise, improving phase extraction accuracy.

## Contribution

It proposes a novel deep learning-based filtering method with multi-stage architecture and integrated regularization for optical fringe pattern denoising.

## Key findings

- Competitive with state-of-the-art denoising methods
- Efficiently preserves fringe features
- Works well on high-density fringe patterns

## Abstract

Optical fringe patterns are often contaminated by speckle noise, making it difficult to accurately and robustly extract their phase fields. To deal with this problem, we propose a filtering method based on deep learning, called optical fringe patterns denoising convolutional neural network (FPD-CNN), for directly removing speckle from the input noisy fringe patterns. Regularization technology is integrated into the design of deep architecture. Specifically, the FPD-CNN method is divided into multiple stages, each stage consists of a set of convolutional layers along with batch normalization and leaky rectified linear unit (Leaky ReLU) activation function. The end-to-end joint training is carried out using the Euclidean loss. Extensive experiments on simulated and experimental optical fringe patterns,especially finer ones with high-density regions, show that the proposed method is competitive with some state-of-the-art denoising techniques in spatial or transform domains, efficiently preserving main features of fringe at a fairly fast speed.