Optimal Transparent Wavelength and Arrangement for Multispectral Filter Array

Yanagi, Yudai; Shinoda, Kazuma; Hasegawa, Madoka; Kato, Shigeo; Ishikawa, Masahiro; Kobara, Hideki; Kobayashi, Naoki

doi:10.2352/issn.2470-1173.2016.15.ipas-024

Cited by 4 publications

(4 citation statements)

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“…In order to prove the efficiency and effectiveness of our MSFA design method, we evaluated our optimized MSFA by comparing the spectral construction error of our MSFA with the errors of three MSFAs designed by three state-of-art methods (Yanagi’s [43], Fu’s [36], and Arad’s [35]) in simulations on the three multispectral image datasets(CAVE [6], Harvard’s [49], and ICVL’s [24]). It is worth noting that for fair comparisons, we did not compare our designed MSFAs with SILIOS and other commercial MSFAs since their candidate filter set is unknown.…”

Section: Resultsmentioning

confidence: 99%

“…However, all of these continuous optimizations are not suitable for the discrete combination problem involved filter selection and channel arrangement. In contrast, other overall design methods [3,43] applied heuristic global search algorithm to find a near-optimal MSFA. Motivated by these overall design methods, we model the total errors in the pipeline of imaging and reconstruction by considering the spectral sensitivities of channels, the spatial arrangement of channels, and the statistical prior of spectral images as a whole.…”

Section: Related Workmentioning

confidence: 99%

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Optimized Multi-Spectral Filter Arrays for Spectral Reconstruction

Xie

et al. 2019

Sensors

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Multispectral filter array (MSFA)-based imaging is a compact, practical technique for snapshot spectral image capturing and reconstruction. The imaging and reconstruction quality is highly influenced by the spectral sensitivities and spatial arrangement of channels on MSFAs, and the used reconstruction method. In order to design a MSFA with high imaging capacity, we propose a sparse representation based approach to optimize spectral sensitivities and spatial arrangement of MSFAs. The proposed approach first overall models the various errors associated with spectral reconstruction, and then uses a global heuristic searching method to optimize MSFAs via minimizing the estimated error of MSFAs. Our MSFA optimization method can select filters from off-the-shelf candidate filter sets while assigning the selected filters to the designed MSFA. Experimental results on three datasets show that the proposed method is more efficient, flexible, and can design MSFAs with lower spectral construction errors when compared with existing state-of-the-art methods. The MSFAs designed by our method show better performance than others even using different spectral reconstruction methods.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimized Multi-Spectral Filter Arrays for Spectral Reconstruction

Xie

et al. 2019

Sensors

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“…Other approaches instead focus on subselecting filters from available candidates during the joint optimization. For example, near-optimal MSFA design was achieved using a heuristic global search algorithm [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Opti-MSFA: a toolbox for generalized design and optimization of multispectral filter arrays

et al. 2022

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Multispectral imaging captures spatial information across a set of discrete spectral channels and is widely utilized across diverse applications such as remote sensing, industrial inspection, and biomedical imaging. Multispectral filter arrays (MSFAs) are filter mosaics integrated atop image sensors that facilitate cost-effective, compact, snapshot multispectral imaging. MSFAs are pre-configured based on application—where filter channels are selected corresponding to targeted absorption spectra—making the design of optimal MSFAs vital for a given application. Despite the availability of many design and optimization approaches for spectral channel selection and spatial arrangement, major limitations remain. There are few robust approaches for joint spectral-spatial optimization, techniques are typically only applicable to limited datasets and most critically, are not available for general use and improvement by the wider community. Here, we reconcile current MSFA design techniques and present Opti-MSFA: a Python-based open-access toolbox for the centralized design and optimization of MSFAs. Opti-MSFA incorporates established spectral-spatial optimization algorithms, such as gradient descent and simulated annealing, multispectral-RGB image reconstruction, and is applicable to user-defined input of spatial-spectral datasets or imagery. We demonstrate the utility of the toolbox by comparing against other published MSFAs using the standard hyperspectral datasets Samson and Jasper Ridge, and further show application on experimentally acquired fluorescence imaging data. In conjunction with end-user input and collaboration, we foresee the continued development of Opti-MSFA for the benefit of the wider research community.

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“…Their method iteratively optimizes the parameters of the Gaussian distribution of each filter. Our previous study [10], [11] assumed that the spectral sensitivity of each filter was an ideal delta function, and simultaneously optimized the number of filters and the center wavelength of each one. However, the optimal spectral sensitivity of each filter with respect to a specific application was not considered.…”

Section: Introductionmentioning

confidence: 99%

Multispectral filter array and demosaicking for pathological images

Shinoda

Ogawa

Yanagi

et al. 2015

2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

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A capturing system with multispectral filter array (MSFA) technology is proposed for shortening the capture time and reducing costs. Therein, a mosaicked image captured using an MSFA is demosaicked to reconstruct multispectral images (MSIs). Joint optimization of the spectral sensitivity of the MSFAs and demosaicking is considered, and pathology-specific multispectral imaging is proposed. This optimizes the MSFA and the demosaicking matrix by minimizing the reconstruction error between the training data of a hematoxylin and eosin-stained pathological tissue and a demosaicked MSI using a cost function. Initially, the spectral sensitivity of the filter array is set randomly and the mosaicked image is obtained from the training data. Subsequently, a reconstructed image is obtained using Wiener estimation. To minimize the reconstruction error, the spectral sensitivity of the filter array and the Wiener estimation matrix are optimized iteratively through an interior-point approach. The effectiveness of the proposed MSFA and demosaicking is demonstrated by comparing the recovered spectrum and RGB image with those obtained using a conventional method.

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Optimal Transparent Wavelength and Arrangement for Multispectral Filter Array

Cited by 4 publications

References 0 publications

Optimized Multi-Spectral Filter Arrays for Spectral Reconstruction

Optimized Multi-Spectral Filter Arrays for Spectral Reconstruction

Opti-MSFA: a toolbox for generalized design and optimization of multispectral filter arrays

Multispectral filter array and demosaicking for pathological images

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