Efficient high-resolution microscopic ghost imaging via sequenced speckle illumination and deep learning from a single noisy image
This study presents a novel approach for achieving high-quality and large-scale microscopic ghost imaging by integrating deep learning-based denoising with computational ghost imaging techniques. By utilizing sequenced random speckle patterns of optimized sizes, we reconstructed large noisy images w...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
|
| Series: | Advanced Optical Technologies |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/aot.2025.1583836/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849683634068389888 |
|---|---|
| author | Sukyoon Oh Sukyoon Oh Tong Tian Tong Tian Zhe Sun Christian Spielmann Christian Spielmann |
| author_facet | Sukyoon Oh Sukyoon Oh Tong Tian Tong Tian Zhe Sun Christian Spielmann Christian Spielmann |
| author_sort | Sukyoon Oh |
| collection | DOAJ |
| description | This study presents a novel approach for achieving high-quality and large-scale microscopic ghost imaging by integrating deep learning-based denoising with computational ghost imaging techniques. By utilizing sequenced random speckle patterns of optimized sizes, we reconstructed large noisy images with fewer patterns while successfully resolving fine details as small as 2.2 μm on a USAF resolution target. To enhance image quality, we incorporated the Deep Neural Network-based Noise2Void (N2V) model, which effectively denoises ghost images without requiring a reference image or a large dataset. By applying the N2V model to a single noisy ghost image, we achieved significant noise reduction, leading to high-resolution and high-quality reconstructions with low computational resources. This method resulted in an average Structural Similarity Index (SSIM) improvement of over 324% and a resolution enhancement exceeding 33% across various target images. The proposed approach proves highly effective in enhancing the clarity and structural integrity of even very low-quality ghost images, paving the way for more efficient and practical implementations of ghost imaging in microscopic applications. |
| format | Article |
| id | doaj-art-aec437dfae4e4e298f47e2c12378c5cb |
| institution | DOAJ |
| issn | 2192-8584 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Advanced Optical Technologies |
| spelling | doaj-art-aec437dfae4e4e298f47e2c12378c5cb2025-08-20T03:23:46ZengFrontiers Media S.A.Advanced Optical Technologies2192-85842025-06-011410.3389/aot.2025.15838361583836Efficient high-resolution microscopic ghost imaging via sequenced speckle illumination and deep learning from a single noisy imageSukyoon Oh0Sukyoon Oh1Tong Tian2Tong Tian3Zhe Sun4Christian Spielmann5Christian Spielmann6Institute of Optics and Quantum Electronics, Abbe Center of Photonics, Friedrich Schiller University, Jena, GermanyGSI Helmholtz Centre for Heavy Ion Research, Darmstadt, GermanyInstitute of Optics and Quantum Electronics, Abbe Center of Photonics, Friedrich Schiller University, Jena, GermanyGSI Helmholtz Centre for Heavy Ion Research, Darmstadt, GermanySchool of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, ChinaInstitute of Optics and Quantum Electronics, Abbe Center of Photonics, Friedrich Schiller University, Jena, GermanyGSI Helmholtz Centre for Heavy Ion Research, Darmstadt, GermanyThis study presents a novel approach for achieving high-quality and large-scale microscopic ghost imaging by integrating deep learning-based denoising with computational ghost imaging techniques. By utilizing sequenced random speckle patterns of optimized sizes, we reconstructed large noisy images with fewer patterns while successfully resolving fine details as small as 2.2 μm on a USAF resolution target. To enhance image quality, we incorporated the Deep Neural Network-based Noise2Void (N2V) model, which effectively denoises ghost images without requiring a reference image or a large dataset. By applying the N2V model to a single noisy ghost image, we achieved significant noise reduction, leading to high-resolution and high-quality reconstructions with low computational resources. This method resulted in an average Structural Similarity Index (SSIM) improvement of over 324% and a resolution enhancement exceeding 33% across various target images. The proposed approach proves highly effective in enhancing the clarity and structural integrity of even very low-quality ghost images, paving the way for more efficient and practical implementations of ghost imaging in microscopic applications.https://www.frontiersin.org/articles/10.3389/aot.2025.1583836/fullghost imaging (GI)deep learningNoise2Voidsingle pixel imagingmicroscopydenoising |
| spellingShingle | Sukyoon Oh Sukyoon Oh Tong Tian Tong Tian Zhe Sun Christian Spielmann Christian Spielmann Efficient high-resolution microscopic ghost imaging via sequenced speckle illumination and deep learning from a single noisy image Advanced Optical Technologies ghost imaging (GI) deep learning Noise2Void single pixel imaging microscopy denoising |
| title | Efficient high-resolution microscopic ghost imaging via sequenced speckle illumination and deep learning from a single noisy image |
| title_full | Efficient high-resolution microscopic ghost imaging via sequenced speckle illumination and deep learning from a single noisy image |
| title_fullStr | Efficient high-resolution microscopic ghost imaging via sequenced speckle illumination and deep learning from a single noisy image |
| title_full_unstemmed | Efficient high-resolution microscopic ghost imaging via sequenced speckle illumination and deep learning from a single noisy image |
| title_short | Efficient high-resolution microscopic ghost imaging via sequenced speckle illumination and deep learning from a single noisy image |
| title_sort | efficient high resolution microscopic ghost imaging via sequenced speckle illumination and deep learning from a single noisy image |
| topic | ghost imaging (GI) deep learning Noise2Void single pixel imaging microscopy denoising |
| url | https://www.frontiersin.org/articles/10.3389/aot.2025.1583836/full |
| work_keys_str_mv | AT sukyoonoh efficienthighresolutionmicroscopicghostimagingviasequencedspeckleilluminationanddeeplearningfromasinglenoisyimage AT sukyoonoh efficienthighresolutionmicroscopicghostimagingviasequencedspeckleilluminationanddeeplearningfromasinglenoisyimage AT tongtian efficienthighresolutionmicroscopicghostimagingviasequencedspeckleilluminationanddeeplearningfromasinglenoisyimage AT tongtian efficienthighresolutionmicroscopicghostimagingviasequencedspeckleilluminationanddeeplearningfromasinglenoisyimage AT zhesun efficienthighresolutionmicroscopicghostimagingviasequencedspeckleilluminationanddeeplearningfromasinglenoisyimage AT christianspielmann efficienthighresolutionmicroscopicghostimagingviasequencedspeckleilluminationanddeeplearningfromasinglenoisyimage AT christianspielmann efficienthighresolutionmicroscopicghostimagingviasequencedspeckleilluminationanddeeplearningfromasinglenoisyimage |