Efficient and robust phase unwrapping method based on SFNet Article Swipe
YOU?
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· 2024
· Open Access
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· DOI: https://doi.org/10.1364/oe.517676
Phase unwrapping is a crucial step in obtaining the final physical information in the field of optical metrology. Although good at dealing with phase with discontinuity and noise, most deep learning-based spatial phase unwrapping methods suffer from the complex model and unsatisfactory performance, partially due to simple noise type for training datasets and limited interpretability. This paper proposes a highly efficient and robust spatial phase unwrapping method based on an improved SegFormer network, SFNet. The SFNet structure uses a hierarchical encoder without positional encoding and a decoder based on a lightweight fully connected multilayer perceptron. The proposed method utilizes the self-attention mechanism of the Transformer to better capture the global relationship of phase changes and reduce errors in the phase unwrapping process. It has a lower parameter count, speeding up the phase unwrapping. The network is trained on a simulated dataset containing various types of noise and phase discontinuity. This paper compares the proposed method with several state-of-the-art deep learning-based and traditional methods in terms of important evaluation indices, such as RMSE and PFS, highlighting its structural stability, robustness to noise, and generalization.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1364/oe.517676
- OA Status
- gold
- Cited By
- 16
- References
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- Related Works
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- OpenAlex ID
- https://openalex.org/W4393040487
Raw OpenAlex JSON
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https://openalex.org/W4393040487Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1364/oe.517676Digital Object Identifier
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Efficient and robust phase unwrapping method based on SFNetWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-21Full publication date if available
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Ziheng Zhang, Xiaoxu Wang, Chengxiu Liu, Ziyu Han, Qingxiong Xiao, Zhilin Zhang, Wenlu Feng, Mingyong Liu, Qianbo LuList of authors in order
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https://doi.org/10.1364/oe.517676Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1364/oe.517676Direct OA link when available
- Concepts
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Optics, Phase (matter), Phase unwrapping, Computer science, Interferometry, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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16Total citation count in OpenAlex
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2025: 11, 2024: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Optics Express |
| best_oa_location.landing_page_url | https://doi.org/10.1364/oe.517676 |
| primary_location.id | doi:10.1364/oe.517676 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S178776955 |
| primary_location.source.issn | 1094-4087 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1094-4087 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Optics Express |
| primary_location.source.host_organization | https://openalex.org/P4310315679 |
| primary_location.source.host_organization_name | Optica Publishing Group |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310315679 |
| primary_location.source.host_organization_lineage_names | Optica Publishing Group |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Optics Express |
| primary_location.landing_page_url | https://doi.org/10.1364/oe.517676 |
| publication_date | 2024-03-21 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W3149301430, https://openalex.org/W2465169776, https://openalex.org/W2922795189, https://openalex.org/W2129849658, https://openalex.org/W2172171271, https://openalex.org/W2039314080, https://openalex.org/W2091764829, https://openalex.org/W2117453234, https://openalex.org/W4312218171, https://openalex.org/W2005113098, https://openalex.org/W2097880538, https://openalex.org/W3169918850, https://openalex.org/W2997457044, https://openalex.org/W2052525354, https://openalex.org/W2008373329, https://openalex.org/W1992306337, https://openalex.org/W1969827956, https://openalex.org/W1986932985, https://openalex.org/W4308478222, https://openalex.org/W3016525206, https://openalex.org/W4321195916, https://openalex.org/W2230653175, https://openalex.org/W2145229137, https://openalex.org/W2042029942, https://openalex.org/W2139382721, https://openalex.org/W2046875048, https://openalex.org/W2036330938, https://openalex.org/W2009281790, https://openalex.org/W2131877057, https://openalex.org/W2560689942, https://openalex.org/W2130204909, https://openalex.org/W2748848225, https://openalex.org/W2944537797, https://openalex.org/W2963881378, https://openalex.org/W2965034661, https://openalex.org/W4385627032, https://openalex.org/W2900139389, https://openalex.org/W3010584812, https://openalex.org/W4283784086, https://openalex.org/W3014641072, https://openalex.org/W2944779716, https://openalex.org/W4285008311, https://openalex.org/W4385754250, https://openalex.org/W3042532487, https://openalex.org/W3214823030, https://openalex.org/W4386856828, https://openalex.org/W4380763457, https://openalex.org/W4288809219, https://openalex.org/W4385391093, https://openalex.org/W4386208046, https://openalex.org/W4206216641, https://openalex.org/W4297996548, https://openalex.org/W4361276967, https://openalex.org/W2990138404 |
| referenced_works_count | 54 |
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| abstract_inverted_index.an | 69 |
| abstract_inverted_index.as | 170 |
| abstract_inverted_index.at | 20 |
| abstract_inverted_index.in | 6, 12, 117, 163 |
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| abstract_inverted_index.of | 15, 102, 111, 144, 165 |
| abstract_inverted_index.on | 68, 88, 137 |
| abstract_inverted_index.to | 45, 105, 179 |
| abstract_inverted_index.up | 129 |
| abstract_inverted_index.The | 74, 95, 133 |
| abstract_inverted_index.and | 26, 40, 52, 61, 84, 114, 146, 160, 172, 181 |
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| abstract_inverted_index.its | 175 |
| abstract_inverted_index.the | 8, 13, 37, 99, 103, 108, 118, 130, 152 |
| abstract_inverted_index.PFS, | 173 |
| abstract_inverted_index.RMSE | 171 |
| abstract_inverted_index.This | 55, 149 |
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| abstract_inverted_index.fully | 91 |
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| abstract_inverted_index.paper | 56, 150 |
| abstract_inverted_index.phase | 23, 32, 64, 112, 119, 131, 147 |
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| abstract_inverted_index.types | 143 |
| abstract_inverted_index.SFNet. | 73 |
| abstract_inverted_index.better | 106 |
| abstract_inverted_index.count, | 127 |
| abstract_inverted_index.errors | 116 |
| abstract_inverted_index.global | 109 |
| abstract_inverted_index.highly | 59 |
| abstract_inverted_index.method | 66, 97, 154 |
| abstract_inverted_index.noise, | 27, 180 |
| abstract_inverted_index.reduce | 115 |
| abstract_inverted_index.robust | 62 |
| abstract_inverted_index.simple | 46 |
| abstract_inverted_index.suffer | 35 |
| abstract_inverted_index.capture | 107 |
| abstract_inverted_index.changes | 113 |
| abstract_inverted_index.complex | 38 |
| abstract_inverted_index.crucial | 4 |
| abstract_inverted_index.dataset | 140 |
| abstract_inverted_index.dealing | 21 |
| abstract_inverted_index.decoder | 86 |
| abstract_inverted_index.encoder | 80 |
| abstract_inverted_index.limited | 53 |
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| abstract_inverted_index.various | 142 |
| abstract_inverted_index.without | 81 |
| abstract_inverted_index.Although | 18 |
| abstract_inverted_index.compares | 151 |
| abstract_inverted_index.datasets | 51 |
| abstract_inverted_index.encoding | 83 |
| abstract_inverted_index.improved | 70 |
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| abstract_inverted_index.proposed | 96, 153 |
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| abstract_inverted_index.training | 50 |
| abstract_inverted_index.utilizes | 98 |
| abstract_inverted_index.SegFormer | 71 |
| abstract_inverted_index.connected | 92 |
| abstract_inverted_index.efficient | 60 |
| abstract_inverted_index.important | 166 |
| abstract_inverted_index.mechanism | 101 |
| abstract_inverted_index.obtaining | 7 |
| abstract_inverted_index.parameter | 126 |
| abstract_inverted_index.partially | 43 |
| abstract_inverted_index.simulated | 139 |
| abstract_inverted_index.structure | 76 |
| abstract_inverted_index.containing | 141 |
| abstract_inverted_index.evaluation | 167 |
| abstract_inverted_index.metrology. | 17 |
| abstract_inverted_index.multilayer | 93 |
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| abstract_inverted_index.robustness | 178 |
| abstract_inverted_index.stability, | 177 |
| abstract_inverted_index.structural | 176 |
| abstract_inverted_index.unwrapping | 1, 33, 65, 120 |
| abstract_inverted_index.Transformer | 104 |
| abstract_inverted_index.information | 11 |
| abstract_inverted_index.lightweight | 90 |
| abstract_inverted_index.perceptron. | 94 |
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| abstract_inverted_index.unwrapping. | 132 |
| abstract_inverted_index.hierarchical | 79 |
| abstract_inverted_index.highlighting | 174 |
| abstract_inverted_index.performance, | 42 |
| abstract_inverted_index.relationship | 110 |
| abstract_inverted_index.discontinuity | 25 |
| abstract_inverted_index.discontinuity. | 148 |
| abstract_inverted_index.learning-based | 30, 159 |
| abstract_inverted_index.self-attention | 100 |
| abstract_inverted_index.unsatisfactory | 41 |
| abstract_inverted_index.generalization. | 182 |
| abstract_inverted_index.state-of-the-art | 157 |
| abstract_inverted_index.interpretability. | 54 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.96704087 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |