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Digital Imaging with Multispectral Filter Array (MSFA) Sensors
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Digital Imaging with Multispectral Filter Array (MSFA) Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 16546

Special Issue Editors

Prof. Dr. Moon Gi Kang

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei Road, Seodaemun Gu, Seoul 03722, Korea
Interests: image deconvolution/restoration; superresolution image reconstruction; multispectral imaging; imaging engines for sensors and display devices
Prof. Dr. Sukho Lee

E-Mail Website
Guest Editor
Division of Computer Engineering, Dongseo University, 47 Jurye Road, Sasang Gu, Busan 47011, Republic of Korea
Interests: image deconvolution/restoration; color image compression; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Current digital imaging systems (including digital cameras) often comprise a monochrome image sensor with a color filter array (CFA) for capturing color information. Bayer CFA is based on the primary color channels (red, green, and blue), and has been widely applied in general digital imaging. Furthermore, various CFAs have been developed for overcoming the physical limitations of Bayer CFA. Multispectral filter array (MSFA) has been recently proposed, which can capture the three primary color channels and additional spectral bands, such as near-infrared and broadband. The MSFA design determines the manner in which multiple bands of light will be received, because digital imaging aspects, such as sensitivity, resolution, and color reproduction, significantly depend on the filter pattern. The MSFA image sensor has a high sensitivity and can thereby enhance spatial resolution by incorporating additional information received from spectral wide bands. Hence, digital imaging based on the MSFA image sensor can provide novel solutions for addressing different conditions of image acquisition, and can be employed in various applications such as military, surveillance, remote sensing, and scientific imaging applications.

Several image processing algorithms have been proposed for improving the quality of digital images. However, digital imaging based on the MSFA image sensor and Bayer CFA has certain drawbacks, such as limited resolution, color degradation, and optical aberration. Depending on the CFA and MSFA design, the sub-sampled raw data may undergo a significant loss of resolution. Demosaicking algorithms are therefore centered on resolution improvement for recovering the resolutions of the original color channels. Additionally, as spectral sensitivity differs for each wavelength, the raw data may require color balancing for reproducing the exact colors of the associated digital image. The incorporation of multiple spectral wavelengths can also generate optical aberrations that need to be rectified. To address these issues, optimal CFA and MSFA design with corresponding image processing algorithms are necessary. Furthermore, algorithms for color correction, high dynamic range imaging, multispectral fusion, sensitivity improvement, and resolution improvement are also important subjects of research in the field of digital imaging.

The objective of this Special Issue is to invite researchers to contribute papers regarding theoretical, methodological, and practical issues associated with optimal CFA and MSFA designing, color demosaicking methods, post-processing tasks for CFA and MSFA, and multispectral fusion methods.

Topics for this Special Issue include (but are not limited to) the following:

  • Optimal filter array design:
    • CFA design
    • MSFA design
  • Color demosaicking/interpolation methods:
    • RGB CFAs (including Bayer CFA)
    • MSFAs (RGBW, RGBNir, etc.)
  • Post-processing for CFAs/MSFAs I:
    • Color balancing
    • Color correction
    • Aberration correction, etc.
  • Post-processing for CFAs / MSFAs II:
    • High dynamic range imaging
    • Resolution improvement
    • Sensitivity improvement, etc.
  • Multispectral fusion methods of multispectral channels:
    • Near-infrared information
    • Broadband information, etc.

Prof. Dr. Moon Gi Kang
Prof. Dr. Sukho Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • CFA
  • MSFA
  • optimal FA design
  • color demosaicking
  • RGB CFAs
  • Bayer CFA
  • RGB-W, RGB-Nir
  • color balancing
  • color correction
  • sensitivity improvement
  • resolution improvement
  • HDR
  • multispectral fusion
  • postprocessing of color sensors

Published Papers (5 papers)

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Research

28 pages, 42170 KiB  
Article
Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors
by Fernando Pérez-Bueno, Miguel Vega, Javier Mateos, Rafael Molina and Aggelos K. Katsaggelos
Sensors 2020, 20(18), 5308; https://doi.org/10.3390/s20185308 - 16 Sep 2020
Cited by 2 | Viewed by 2609
Abstract
Pansharpening is a technique that fuses a low spatial resolution multispectral image and a high spatial resolution panchromatic one to obtain a multispectral image with the spatial resolution of the latter while preserving the spectral information of the multispectral image. In this paper [...] Read more.
Pansharpening is a technique that fuses a low spatial resolution multispectral image and a high spatial resolution panchromatic one to obtain a multispectral image with the spatial resolution of the latter while preserving the spectral information of the multispectral image. In this paper we propose a variational Bayesian methodology for pansharpening. The proposed methodology uses the sensor characteristics to model the observation process and Super-Gaussian sparse image priors on the expected characteristics of the pansharpened image. The pansharpened image, as well as all model and variational parameters, are estimated within the proposed methodology. Using real and synthetic data, the quality of the pansharpened images is assessed both visually and quantitatively and compared with other pansharpening methods. Theoretical and experimental results demonstrate the effectiveness, efficiency, and flexibility of the proposed formulation. Full article
(This article belongs to the Special Issue Digital Imaging with Multispectral Filter Array (MSFA) Sensors)
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16 pages, 9414 KiB  
Article
Pyramid Inter-Attention for High Dynamic Range Imaging
by Sungil Choi, Jaehoon Cho, Wonil Song, Jihwan Choe, Jisung Yoo and Kwanghoon Sohn
Sensors 2020, 20(18), 5102; https://doi.org/10.3390/s20185102 - 07 Sep 2020
Cited by 11 | Viewed by 3299
Abstract
This paper proposes a novel approach to high-dynamic-range (HDR) imaging of dynamic scenes to eliminate ghosting artifacts in HDR images when in the presence of severe misalignment (large object or camera motion) in input low-dynamic-range (LDR) images. Recent non-flow-based methods suffer from ghosting [...] Read more.
This paper proposes a novel approach to high-dynamic-range (HDR) imaging of dynamic scenes to eliminate ghosting artifacts in HDR images when in the presence of severe misalignment (large object or camera motion) in input low-dynamic-range (LDR) images. Recent non-flow-based methods suffer from ghosting artifacts in the presence of large object motion. Flow-based methods face the same issue since their optical flow algorithms yield huge alignment errors. To eliminate ghosting artifacts, we propose a simple yet effective alignment network for solving the misalignment. The proposed pyramid inter-attention module (PIAM) performs alignment of LDR features by leveraging inter-attention maps. Additionally, to boost the representation of aligned features in the merging process, we propose a dual excitation block (DEB) that recalibrates each feature both spatially and channel-wise. Exhaustive experimental results demonstrate the effectiveness of the proposed PIAM and DEB, achieving state-of-the-art performance in terms of producing ghost-free HDR images. Full article
(This article belongs to the Special Issue Digital Imaging with Multispectral Filter Array (MSFA) Sensors)
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16 pages, 1339 KiB  
Article
Joint Demosaicing and Denoising Based on Interchannel Nonlocal Mean Weighted Moving Least Squares Method
by Yeahwon Kim, Hohyung Ryu, Sunmi Lee and Yeon Ju Lee
Sensors 2020, 20(17), 4697; https://doi.org/10.3390/s20174697 - 20 Aug 2020
Cited by 4 | Viewed by 2938
Abstract
Nowadays, the sizes of pixel sensors in digital cameras are decreasing as the resolution of the image sensor increases. Due to the decreased size, the pixel sensors receive less light energy, which makes it more sensitive to thermal noise. Even a small amount [...] Read more.
Nowadays, the sizes of pixel sensors in digital cameras are decreasing as the resolution of the image sensor increases. Due to the decreased size, the pixel sensors receive less light energy, which makes it more sensitive to thermal noise. Even a small amount of noise in the color filter array (CFA) can have a significant effect on the reconstruction of the color image, as two-thirds of the missing data would have to be reconstructed from noisy data; because of this, direct denoising would need to be performed on the raw CFA to obtain a high-resolution color image. In this paper, we propose an interchannel nonlocal weighted moving least square method for the noise removal of the raw CFA. The proposed method is our first attempt of applying a two dimensional (2-D) polynomial approximation to denoising the CFA. Previous works make use of 2-D linear or directional 1-D polynomial approximations. The reason that 2-D polynomial approximation methods have not been applied to this problem is the difficulty of the weight control in the 2-D polynomial approximation method, as a small amount of noise can have a large effect on the approximated 2-D shape. This makes CFA denoising more important, as the approximated 2-D shape has to be reconstructed from only one-third of the original data. To address this problem, we propose a method that reconstructs the approximated 2-D shapes corresponding to the RGB color channels based on the measure of the similarities of the patches directly on the CFA. By doing so, the interchannel information is incorporated into the denoising scheme, which results in a well-controlled and higher order of polynomial approximation of the color channels. Compared to other nonlocal-mean-based denoising methods, the proposed method uses an extra reproducing constraint, which guarantees a certain degree of the approximation order; therefore, the proposed method can reduce the number of false reconstruction artifacts that often occur in nonlocal-mean-based denoising methods. Experimental results demonstrate the performance of the proposed algorithm. Full article
(This article belongs to the Special Issue Digital Imaging with Multispectral Filter Array (MSFA) Sensors)
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18 pages, 50900 KiB  
Article
Demosaicing of RGBW Color Filter Array Based on Rank Minimization with Colorization Constraint
by Hansol Kim, Sukho Lee and Moon Gi Kang
Sensors 2020, 20(16), 4458; https://doi.org/10.3390/s20164458 - 10 Aug 2020
Cited by 4 | Viewed by 3340
Abstract
Recently, the white (w) channel has been incorporated in various forms into color filter arrays (CFAs). The advantage of using theWchannel is thatWpixels have less noise than red (R), green (G), or blue (B) (RGB) pixels; therefore, under low-light conditions, pixels with high [...] Read more.
Recently, the white (w) channel has been incorporated in various forms into color filter arrays (CFAs). The advantage of using theWchannel is thatWpixels have less noise than red (R), green (G), or blue (B) (RGB) pixels; therefore, under low-light conditions, pixels with high fidelity can be obtained. However, RGBW CFAs normally suffer from spatial resolution degradation due to a smaller number of color pixels than in RGB CFAs. Therefore, even though the reconstructed colors have higher sensitivity, which results in larger Color Peak Signal-to-Noise Ratio (CPSNR) values, there are some color aliasing artifacts due to a low resolution. In this paper, we propose a rank minimization-based color interpolation method with a colorization constraint for the RGBW format with a large number ofWpixels. The rank minimization can achieve a broad interpolation and preserve the structure in the image, and it thereby eliminates the color artifacts. However, the colors fade from this global process. Therefore, we further incorporate a colorization constraint into the rank minimization process for the better reproduction of the colors. The experimental results show that the images can be reconstructed well, even from noisy pattern images obtained under low-light conditions. Full article
(This article belongs to the Special Issue Digital Imaging with Multispectral Filter Array (MSFA) Sensors)
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14 pages, 69145 KiB  
Article
Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network
by Yunjin Park, Sukho Lee, Byeongseon Jeong and Jungho Yoon
Sensors 2020, 20(10), 2970; https://doi.org/10.3390/s20102970 - 24 May 2020
Cited by 8 | Viewed by 3611
Abstract
A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array. Recently, inspired by the success of deep learning in many [...] Read more.
A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array. Recently, inspired by the success of deep learning in many image processing tasks, there has been research to apply convolutional neural networks (CNNs) to the task of joint demosaicing and denoising. However, such CNNs need many training data to be trained, and work well only for patterned images which have the same amount of noise they have been trained on. In this paper, we propose a variational deep image prior network for joint demosaicing and denoising which can be trained on a single patterned image and works for patterned images with different levels of noise. We also propose a new RGB color filter array (CFA) which works better with the proposed network than the conventional Bayer CFA. Mathematical justifications of why the variational deep image prior network suits the task of joint demosaicing and denoising are also given, and experimental results verify the performance of the proposed method. Full article
(This article belongs to the Special Issue Digital Imaging with Multispectral Filter Array (MSFA) Sensors)
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