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dc.contributor.authorArguello, Henry
dc.contributor.authorBacca, Jorge
dc.contributor.authorKariyawasam, Hasindu
dc.contributor.authorVargas, Edwin
dc.contributor.authorMarquez, Miguel
dc.contributor.authorHettiarachchi, Ramith
dc.contributor.authorGarcia, Hans
dc.contributor.authorHerath, Kithmini
dc.contributor.authorHaputhanthri, Udith
dc.contributor.authorAhluwalia, Balpreet Singh
dc.contributor.authorSo, Peter
dc.contributor.authorWadduwage, Dushan N.
dc.contributor.authorEdussooriya, Chamira U.S.
dc.date.accessioned2024-03-14T10:15:27Z
dc.date.available2024-03-14T10:15:27Z
dc.date.issued2023-02-27
dc.description.abstractComputational optical imaging (COI) systems leverage optical coding elements (CEs) in their setups to encode a high-dimensional scene in a single or in multiple snapshots and decode it by using computational algorithms. The performance of COI systems highly depends on the design of its main components: the CE pattern and the computational method used to perform a given task. Conventional approaches rely on random patterns or analytical designs to set distribution of the CE. However, the available data and algorithm capabilities of deep neural networks (DNNs) have opened a new horizon in CE data-driven designs that jointly consider the optical and computational decoders. Specifically, by modeling the COI measurements through a fully differentiable image-formation model that considers the physics-based propagation of light and its interaction with the CEs, the parameters that define the CE and the computational decoder can be optimized in an end-to-end (E2E) manner. Moreover, by optimizing just CEs in the same framework, inference tasks can be performed from pure optics. This work surveys the recent advances in CE data-driven design and provides guidelines on how to parameterize different optical elements to include them in the E2E framework. As the E2E framework can handle different inference applications by changing the loss function and the DNN, we present low-level tasks such as spectral imaging reconstruction or high-level tasks such as pose estimation with privacy preservation enhanced by using optimal task-based optical architectures. Finally, we illustrate classification and 3D object-recognition applications performed at the speed of the light using all-optics DNNs.en_US
dc.identifier.citationArguello, Bacca, Kariyawasam, Vargas, Marquez, Hettiarachchi, Garcia, Herath, Haputhanthri, Ahluwalia, So, Wadduwage, Edussooriya. Deep Optical Coding Design in Computational Imaging: A data-driven framework. IEEE Signal Processing Magazine. 2023;40(2):75-88en_US
dc.identifier.cristinIDFRIDAID 2157458
dc.identifier.doi10.1109/MSP.2022.3200173
dc.identifier.issn1053-5888
dc.identifier.issn1558-0792
dc.identifier.urihttps://hdl.handle.net/10037/33158
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE Signal Processing Magazine
dc.relation.projectIDNorges forskningsråd: 309802en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.titleDeep Optical Coding Design in Computational Imaging: A data-driven frameworken_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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