Rapid and robust two-dimensional phase unwrapping via deep learning

Zhang, Teng; Jiang, Shaowei; Zhao, Zixin; Dixit, Krishna; Zhou, Xiaofei; Hou, Jia; Zhang, Yongbing; Yan, Chenggang

doi:10.1364/oe.27.023173

Cited by 120 publications

(45 citation statements)

References 14 publications

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“…In order to make better use of higher semantic features extracted by the encoder, an 1 × 1 convolution is employed to reduce the number of channels. After the concatenation, a 3 × 3 convolution is applied to refine the features, ending with another bilinear upsampling by a factor of 4 to obtain the resolution of the input image [48,52]; see Figure 6. Figure 6.…”

Section: Deeplabv3+ With the Xception Backbonementioning

confidence: 99%

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

Torres

Feitosa

Happ

et al. 2020

Sensors

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This study proposes and evaluates five deep fully convolutional networks (FCNs) for the semantic segmentation of a single tree species: SegNet, U-Net, FC-DenseNet, and two DeepLabv3+ variants. The performance of the FCN designs is evaluated experimentally in terms of classification accuracy and computational load. We also verify the benefits of fully connected conditional random fields (CRFs) as a post-processing step to improve the segmentation maps. The analysis is conducted on a set of images captured by an RGB camera aboard a UAV flying over an urban area. The dataset also contains a mask that indicates the occurrence of an endangered species called Dipteryx alata Vogel, also known as cumbaru, taken as the species to be identified. The experimental analysis shows the effectiveness of each design and reports average overall accuracy ranging from 88.9% to 96.7%, an F1-score between 87.0% and 96.1%, and IoU from 77.1% to 92.5%. We also realize that CRF consistently improves the performance, but at a high computational cost.

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Section: Deeplabv3+ With the Xception Backbonementioning

confidence: 99%

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

Torres

Feitosa

Happ

et al. 2020

Sensors

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“…Recently, deep learning methods for PU have appeared in the literature as well. To the best of our knowledge the first attempt has been proposed in [10], where the PU problem was converted into a segmentation task. In Yan et al [11], the authors embedded phase denoising and wrap count reconstruction in a single framework.…”

Section: Introductionmentioning

confidence: 99%

A CNN-Based Coherence-Driven Approach for InSAR Phase Unwrapping

Sica

Cellini

Scarpa

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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Phase unwrapping (PU) is among the most critical tasks in synthetic aperture radar (SAR) interferometry (InSAR). Due to the presence of noise, the interferogram usually presents phase inconsistencies, also called residues, which imply a nonunivocal solution. This work investigates the PU problem from a semantic segmentation perspective by exploiting convolutional neural network (CNN) models. In particular, by exploiting a popular deep-learning architecture, we introduce the interferometric coherence as an input feature and analyze the performance increase against classical methods. For the network training, we generate a variegated data set by introducing a controlled number of phase residues, and considering both synthetic and real InSAR data. Eventually, we compare the proposed method to state-of-the-art algorithms on synthetic and real InSAR data taken from the TanDEM-X mission, obtaining encouraging results.

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“…Nevertheless, this network is not publicly available, and thus cannot be tested on other types of biological cells. The concept of deep-learning phase unwrappers was recently demonstrated for other applications as well, including 2-D phase unwrapping in optical metrology [41,42] and lens-free imaging [43], as well as temporal phase unwrapping in fringe projection profilometry [44].…”

Section: Introductionmentioning

confidence: 99%

PhUn-Net: ready-to-use neural network for unwrapping quantitative phase images of biological cells

Dardikman-Yoffe

Roitshtain

Mirsky

et al. 2020

Biomed. Opt. Express

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We present a deep-learning approach for solving the problem of 2π phase ambiguities in two-dimensional quantitative phase maps of biological cells, using a multi-layer encoderdecoder residual convolutional neural network. We test the trained network, PhUn-Net, on various types of biological cells, captured with various interferometric setups, as well as on simulated phantoms. These tests demonstrate the robustness and generality of the network, even for cells of different morphologies or different illumination conditions than PhUn-Net has been trained on. In this paper, for the first time, we make the trained network publicly available in a global format, such that it can be easily deployed on every platform, to yield fast and robust phase unwrapping, not requiring prior knowledge or complex implementation. By this, we expect our phase unwrapping approach to be widely used, substituting conventional and more time-consuming phase unwrapping algorithms.

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Rapid and robust two-dimensional phase unwrapping via deep learning

Cited by 120 publications

References 14 publications

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

A CNN-Based Coherence-Driven Approach for InSAR Phase Unwrapping

PhUn-Net: ready-to-use neural network for unwrapping quantitative phase images of biological cells

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