Learning-based Noise-aware Lightweight Exposure Strategy for High Dynamic Range Imaging.
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| Title: | Learning-based Noise-aware Lightweight Exposure Strategy for High Dynamic Range Imaging. |
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| Authors: | Jieyu Li1 jli13@alumni.nd.edu, Ruiwen Zhen1, Stevenson, Robert1, Jinwei Gu2 |
| Source: | Journal of Imaging Science & Technology. Jul/Aug2025, Vol. 69 Issue 4, p1-11. 11p. |
| Subjects: | High dynamic range imaging, Signal-to-noise ratio, Image processing, Machine learning, Photographic exposure, Real-time computing, Image quality analysis |
| Abstract: | Real-world scenes typically have a larger dynamic range than what a camera can capture. Temporally and spatially varying exposures have become widely used techniques to capture high dynamic range (HDR) images. One of the key questions is what the optimal set of exposure settings should be in order to achieve good image quality. In response to this question, this paper introduces a lightweight learning-based exposure strategy network. The proposed network is designed to optimize the exposure strategy for direct fusion of standard dynamic range (SDR) images without access to RAW-domain images. Unlike most of the direct fusion exposure strategies that primarily focus on tone optimization alone, the proposed method also incorporates the worst-case signal-to-noise ratio (SNR) in the loss function design. This ensures that the SNR remains consistently above an acceptable threshold while enabling visually pleasing tones in lower noise regions. This lightweight network achieves a significantly shorter inference time compared to other state-of-the-art methods. It is a more practical HDR enhancement technique for real-time and on-device applications. The code can be found at https://github.com/JieyuLi/exposure-bracketing-strategy. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Imaging Science & Technology is the property of International Society for Imaging Science & Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 188510676 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Learning-based Noise-aware Lightweight Exposure Strategy for High Dynamic Range Imaging. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jieyu+Li%22">Jieyu Li</searchLink><relatesTo>1</relatesTo><i> jli13@alumni.nd.edu</i><br /><searchLink fieldCode="AR" term="%22Ruiwen+Zhen%22">Ruiwen Zhen</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Stevenson%2C+Robert%22">Stevenson, Robert</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Jinwei+Gu%22">Jinwei Gu</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Imaging+Science+%26+Technology%22">Journal of Imaging Science & Technology</searchLink>. Jul/Aug2025, Vol. 69 Issue 4, p1-11. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22High+dynamic+range+imaging%22">High dynamic range imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Photographic+exposure%22">Photographic exposure</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Image+quality+analysis%22">Image quality analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Real-world scenes typically have a larger dynamic range than what a camera can capture. Temporally and spatially varying exposures have become widely used techniques to capture high dynamic range (HDR) images. One of the key questions is what the optimal set of exposure settings should be in order to achieve good image quality. In response to this question, this paper introduces a lightweight learning-based exposure strategy network. The proposed network is designed to optimize the exposure strategy for direct fusion of standard dynamic range (SDR) images without access to RAW-domain images. Unlike most of the direct fusion exposure strategies that primarily focus on tone optimization alone, the proposed method also incorporates the worst-case signal-to-noise ratio (SNR) in the loss function design. This ensures that the SNR remains consistently above an acceptable threshold while enabling visually pleasing tones in lower noise regions. This lightweight network achieves a significantly shorter inference time compared to other state-of-the-art methods. It is a more practical HDR enhancement technique for real-time and on-device applications. The code can be found at https://github.com/JieyuLi/exposure-bracketing-strategy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Imaging Science & Technology is the property of International Society for Imaging Science & Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2352/J.ImagingSci.Technol.2025.69.4.040510 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: High dynamic range imaging Type: general – SubjectFull: Signal-to-noise ratio Type: general – SubjectFull: Image processing Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Photographic exposure Type: general – SubjectFull: Real-time computing Type: general – SubjectFull: Image quality analysis Type: general Titles: – TitleFull: Learning-based Noise-aware Lightweight Exposure Strategy for High Dynamic Range Imaging. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jieyu Li – PersonEntity: Name: NameFull: Ruiwen Zhen – PersonEntity: Name: NameFull: Stevenson, Robert – PersonEntity: Name: NameFull: Jinwei Gu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul/Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10623701 Numbering: – Type: volume Value: 69 – Type: issue Value: 4 Titles: – TitleFull: Journal of Imaging Science & Technology Type: main |
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