Guided Filter Demosaicking For Fourier Spectral Filter Array

Jia, Jie; Ni, Chuan; Sarangan, Andrew; Hirakawa, Keigo

doi:10.2352/issn.2470-1173.2016.2.vipc-228

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…(ii) The considered MSFA has already been realized in hardware as a prototype sensor [ 7 ]. (iii) Jia et al have also used the same MSFA arrangement and have reported its effectiveness by the frequency domain analysis of the MSFA [ 67 , 68 ]. These facts suggest the potential of the considered MSFA.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking

Monno

Kiku

Tanaka

et al. 2017

Sensors

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Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking.

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Section: Related Workmentioning

confidence: 99%

Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking

Monno

Kiku

Tanaka

et al. 2017

Sensors

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show abstract

“…51,52 Compressive sensing to compensate for the increasing sparsity have also been used successfully. [53][54][55] Specific to technology, such as Fourier SFA 56,57 and hybrid multi-camera approaches 58 and specific to application demosaicing 59 have also been developed.…”

Section: Introductionmentioning

confidence: 99%

N-LMMSE Demosaicing for Spectral Filter Arrays

Amba¹,

Thomas²,

Alleysson³

2017

jist

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Abstract. Spectral filter array (SFA) technology requires development on demosaicing. The authors extend the linear minimum mean square error with neighborhood method to the spectral dimension. They demonstrate that the method is fast and general on Raw SFA images that span the visible and near infra-red part of the electromagnetic range. The method is quantitatively evaluated in simulation first, then the authors evaluate it on real data by the use of non-reference image quality metrics applied on each band. Resulting images show a much better reconstruction of text and high frequencies at the expense of a zipping effect, compared to the benchmark binary-tree method.

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Fourier Multispectral Imaging: Measuring Spectra, One Sinusoid at a Time

Hirakawa

2017

Lecture Notes in Computer Science

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Guided Filter Demosaicking For Fourier Spectral Filter Array

Abstract: We recently introduced a spectral filter array design for single-shot multispectral imaging that is based on Fourier transform spectroscopy. In this article, we investigate feasibility of guided filter demosaicking for our SFA design.

Cited by 5 publications

References 9 publications

Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking

Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking

N-LMMSE Demosaicing for Spectral Filter Arrays

Fourier Multispectral Imaging: Measuring Spectra, One Sinusoid at a Time

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