Improved Error Reduction and Hybrid Input Output Algorithms for Phase Retrieval by including a Sparse Dictionary Learning-Based Inpainting Method
The phase retrieval (PR), reconstructing an object from its Fourier magnitudes, is equivalent to a nonlinear inverse problem. In this paper, we proposed a two-step algorithm that traditional ER/HIO iteration plays as the coarse feature reconstruction, whereas the KSVD-based inpainting technique deal...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | International Journal of Optics |
| Online Access: | http://dx.doi.org/10.1155/2020/3481830 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832550357878702080 |
|---|---|
| author | Jian-Jia Su Chung-Hao Tien |
| author_facet | Jian-Jia Su Chung-Hao Tien |
| author_sort | Jian-Jia Su |
| collection | DOAJ |
| description | The phase retrieval (PR), reconstructing an object from its Fourier magnitudes, is equivalent to a nonlinear inverse problem. In this paper, we proposed a two-step algorithm that traditional ER/HIO iteration plays as the coarse feature reconstruction, whereas the KSVD-based inpainting technique deals with the fine feature set accordingly. Since the KSVD allows the content of oversampled dictionary with sparse representation to adaptively fit a given set of object examples, as long as the ER/HIO algorithms provide decent object estimation at early stage, the pixels violating the object constraint can be restored with superior image quality. The numerical analyses demonstrated the effectiveness of ER + KSVD and HIO + KSVD through multiple independent initial Fourier phases. With its versatility and simplicity, the proposed method can be generalized to be implemented with more PR state-of-the-arts. |
| format | Article |
| id | doaj-art-6cbf38adf2b14f6e926f61f3faaff761 |
| institution | Kabale University |
| issn | 1687-9384 1687-9392 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Optics |
| spelling | doaj-art-6cbf38adf2b14f6e926f61f3faaff7612025-02-03T06:06:54ZengWileyInternational Journal of Optics1687-93841687-93922020-01-01202010.1155/2020/34818303481830Improved Error Reduction and Hybrid Input Output Algorithms for Phase Retrieval by including a Sparse Dictionary Learning-Based Inpainting MethodJian-Jia Su0Chung-Hao Tien1Department of Photonics, College of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu 30010, TaiwanDepartment of Photonics, College of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu 30010, TaiwanThe phase retrieval (PR), reconstructing an object from its Fourier magnitudes, is equivalent to a nonlinear inverse problem. In this paper, we proposed a two-step algorithm that traditional ER/HIO iteration plays as the coarse feature reconstruction, whereas the KSVD-based inpainting technique deals with the fine feature set accordingly. Since the KSVD allows the content of oversampled dictionary with sparse representation to adaptively fit a given set of object examples, as long as the ER/HIO algorithms provide decent object estimation at early stage, the pixels violating the object constraint can be restored with superior image quality. The numerical analyses demonstrated the effectiveness of ER + KSVD and HIO + KSVD through multiple independent initial Fourier phases. With its versatility and simplicity, the proposed method can be generalized to be implemented with more PR state-of-the-arts.http://dx.doi.org/10.1155/2020/3481830 |
| spellingShingle | Jian-Jia Su Chung-Hao Tien Improved Error Reduction and Hybrid Input Output Algorithms for Phase Retrieval by including a Sparse Dictionary Learning-Based Inpainting Method International Journal of Optics |
| title | Improved Error Reduction and Hybrid Input Output Algorithms for Phase Retrieval by including a Sparse Dictionary Learning-Based Inpainting Method |
| title_full | Improved Error Reduction and Hybrid Input Output Algorithms for Phase Retrieval by including a Sparse Dictionary Learning-Based Inpainting Method |
| title_fullStr | Improved Error Reduction and Hybrid Input Output Algorithms for Phase Retrieval by including a Sparse Dictionary Learning-Based Inpainting Method |
| title_full_unstemmed | Improved Error Reduction and Hybrid Input Output Algorithms for Phase Retrieval by including a Sparse Dictionary Learning-Based Inpainting Method |
| title_short | Improved Error Reduction and Hybrid Input Output Algorithms for Phase Retrieval by including a Sparse Dictionary Learning-Based Inpainting Method |
| title_sort | improved error reduction and hybrid input output algorithms for phase retrieval by including a sparse dictionary learning based inpainting method |
| url | http://dx.doi.org/10.1155/2020/3481830 |
| work_keys_str_mv | AT jianjiasu improvederrorreductionandhybridinputoutputalgorithmsforphaseretrievalbyincludingasparsedictionarylearningbasedinpaintingmethod AT chunghaotien improvederrorreductionandhybridinputoutputalgorithmsforphaseretrievalbyincludingasparsedictionarylearningbasedinpaintingmethod |