PSNet: a deep learning model based digital phase shifting algorithm from a single fringe image
As the gold standard for phase retrieval, phase-shifting algorithm (PS) has been widely used in optical interferometry, fringe projection profilometry, etc. However, capturing multiple fringe patterns in PS limits the algorithm to only a narrow range of application. To this end, a deep learning (DL) model based digital PS algorithm from only a single fringe image is proposed. By training on a simulated dataset of PS fringe patterns, the learnt model, denoted PSNet, can predict fringe patterns with other PS steps when given a pattern with the first PS step. Simulation and experiment results demonstrate the PSNet's promising performance on accurate prediction of digital PS patterns, and robustness to complex scenarios such as surfaces with varying curvature and reflectance.
Code (0)
등록된 구현이 없습니다.
Tasks
RetrievalSimilar Papers 제목 키워드 기반
Capsule Neural Networks as Noise Stabilizer for Time Series Data
Capsule Neural Networks utilize capsules, which bind neurons into a single vector and learn position equivariant features, which makes them more robust than original Convolutional Neural Networks. CapsNets employ an affi…
Adversarial AttackTime SeriesTime Series AnalysisShifting Capsule Networks from the Cloud to the Deep Edge
Capsule networks (CapsNets) are an emerging trend in image processing. In contrast to a convolutional neural network, CapsNets are not vulnerable to object deformation, as the relative spatial information of the objects …
QuantizationPT-CapsNet: A Novel Prediction-Tuning Capsule Network Suitable for Deeper Architectures
Capsule Networks (CapsNets) create internal representations by parsing inputs into various instances at different resolution levels via a two-phase process -- part-whole transformation and hierarchical component rout…
object-detectionObject DetectionSemantic SegmentationHigh-resolution single-shot phase-shifting interference microscopy using deep neural network for quantitative phase imaging of biological samples
White light phase-shifting interference microscopy (WL-PSIM) is a prominent technique for high-resolution quantitative phase imaging (QPI) of industrial and biological specimens. However, multiple interferograms with acc…
RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization
Pathological diagnosis is the gold standard for tumor diagnosis, and nucleus instance segmentation is a key step in digital pathology analysis and pathological diagnosis. However, the computational efficiency of the mode…
Computational EfficiencyInstance SegmentationSemantic Segmentation