Multi-distance phase retrieval with a weighted shrink-wrap constraint

Guo, Cheng; Zhao, Yixuan; Tan, Jiubin; Liu, Shutian; Liu, Zhengjun

doi:10.1016/j.optlaseng.2018.09.017

Cited by 15 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, for an image of size of H W  , this approach requires only M sampling data ( M H W   ) corresponding to M frames of measured patterns. The images are reconstructed while features are extracted and used for recognition tasks, such as image classification, object detection, and semantic segmentation, and the matrix of the measured patterns, as a mapping function from the scene image to the compressed domain, can be used to linearly encode the image information [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Image Recognition Based on Compressive Imaging and Optimal Feature Selection

Jiao

Cheng

et al. 2022

IEEE Photonics J.

View full text Add to dashboard Cite

The measurement matrix in compressive imaging controls the crucial feature information for high performance recognition. In this study, a deterministic orthogonal measurement matrix design method using the discrete cosine transform and a compressive feature selection scheme are proposed to implement high-end computational optics for imaging. The selection scheme systematically evaluates the recognition importance for the frequency features, combined with a scaling of the contribution of the various coefficients used to produce a base matrix for the new group of measuring patterns, which ensures the minimal recognition difference for each individual order of frequency filters and combining a relatively complex expression to quickly find the best quantization values. The model parameters are gradually adjusted and eventually converge to the best result through training with a large number of pre-determined samples from the dataset and backpropagating the feature selection loss along with the recognition loss, and the data processing capabilities can be enhanced because the measurement matrix is a priori information for the recognition phase. The systematic ability of the proposed technique was verified through simulations and experiments on two standard datasets: MNIST and CIFAR-10. The results show that the proposed method outperforms stateof-the-art methods in terms of both the model complexity and classification accuracy, which indicates that our study provides a new practical solution for compressive imaging recognition.

show abstract

Section: Introductionmentioning

confidence: 99%

Image Recognition Based on Compressive Imaging and Optimal Feature Selection

Jiao

Cheng

et al. 2022

IEEE Photonics J.

View full text Add to dashboard Cite

show abstract

“…Recently, multi-intensity iterative algorithms [7][8][9][10] have shown stronger noise robustness, however, it is easy to bring an aliasing artifact into the system. Here the diffraction intensity patterns of an object at different distances are employed to improve the convergence of phase retrieval [11][12][13][14][15][16][17][18]. The single-beam, multiple-intensity reconstruction (SBMIR) algorithm [10] belongs to serial iteration.…”

Section: Introductionmentioning

confidence: 99%

Image Reconstruction Using Autofocus in Single-Lens System

Zhou

Xiu

et al. 2022

Applied Sciences

Self Cite

View full text Add to dashboard Cite

To reconstruct the wavefront in a single-lens coherent diffraction imaging (CDI) system, we propose a closed-loop cascaded iterative engine (CIE) algorithm based on the known information of the imaging planes. The precision of diffraction distance is an important prerequisite for a perfect reconstruction of samples. For coherent diffraction imaging with a lens, autofocus is investigated to accurately determine the object distance and image distance. For the case of only the object distance being unknown, a diffuser is used to scatter the coherent beam for speckle illumination to improve the performance of autofocus. The optimal object distance is obtained stably and robustly by combing speckle imaging with clarity evaluation functions. SSIM and MSE, using the average pixel value of the reconstructed data set as a reference, are applied on two-unknown-distance autofocus. Simulation and experiment results are presented to prove the feasibility of the CIE and proposed auto-focusing method.

show abstract

“…To overcome these restrictions, multi-image phase retrieval techniques that use different kinds of diversities in data collection were proposed [9][10][11][12][13][14][15][16][17][18][19][20][21]. Methods to generate multiple-diversity 2 of 11 intensity measurements include, among others, illumination area overlap [9,10], multiple recording distance [11][12][13][14], multiple wavelength [15], and illumination beam tilting [16,17]. Except for the multiple wavelength method, the other three types of methods require employing mechanical platforms, which inevitably lead to low acquisition speed and cause potential mechanical error.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Phase Retrieval Method Based on Random Phase Modulation

Yan

Peng

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

The phase retrieval method based on random phase modulation can wipe out any ambiguity and stagnation problem in reconstruction. However, the two existing reconstruction algorithms for the random phase modulation method are suffering from problems. The serial algorithm from the spread-spectrum phase retrieval method can realize rapid convergence but has poor noise immunity. Although there is a parallel framework that can suppress noise, the convergence speed is slow.Here, we propose a random phase modulation phase retrieval method based on a serial-parallel cascaded reconstruction framework to simultaneously achieve quality imaging and rapid convergence. The proposed serial-parallel cascaded method uses the phased result from the serial algorithm to serve as the initialization of the subsequent parallel process. Simulations and experiments demonstrate that the superiorities of both serial and parallel algorithms are fetched by the proposed serial-parallel cascaded method. In the end, we analyze the effect of iteration numbers from the serial process on the reconstruction performance to find the optimal allocation scope of iteration numbers.

show abstract

Multi-distance phase retrieval with a weighted shrink-wrap constraint

Cited by 15 publications

References 25 publications

Image Recognition Based on Compressive Imaging and Optimal Feature Selection

Image Recognition Based on Compressive Imaging and Optimal Feature Selection

Image Reconstruction Using Autofocus in Single-Lens System

Enhanced Phase Retrieval Method Based on Random Phase Modulation

Contact Info

Product

Resources

About