Convolutional sparse coding for demosaicking with panchromatic pixels
Image demosaicking is the problem of reconstructing color images from raw images captured by a digital camera covered by a Color Filter Array (CFA). CFAs allow only part of the incident light to transmit, so they will lose some incident light. Recently, CFAs with panchromatic pixels are used to avoi...
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| Vydáno v: | Signal processing. Image communication Ročník 77; s. 20 - 27 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Amsterdam
Elsevier B.V
01.09.2019
Elsevier BV |
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| ISSN: | 0923-5965, 1879-2677 |
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| Abstract | Image demosaicking is the problem of reconstructing color images from raw images captured by a digital camera covered by a Color Filter Array (CFA). CFAs allow only part of the incident light to transmit, so they will lose some incident light. Recently, CFAs with panchromatic pixels are used to avoid excessive loss of light. These CFAs’ color is sampled at a sparse set of locations, making demosaicking more challenging. In this paper, we propose a Convolutional Sparse Coding based method for Demosaicking (CSCD) with panchromatic pixels. The CSCD learns a set of filters to decompose training images into color sparse feature maps, then it estimates a set of feature maps of the to-be-reconstructed raw images and gets color images. Convolutional Sparse Coding (CSC) commonly operates on gray images. Naively extending CSC to color images will make the learned filters to be gray, which means that the values of the red, green, and blue channels are almost equal. To address this issue, we add a regularization term to penalize gray filters. We use the Alternating Direction Method of Multipliers (ADMM) to solve the CSCD. We compare the proposed CSCD with the demosaicking methods that are applicable to CFAs with panchromatic pixels. Experimental results validate its advantages in terms of CPSNR and visual quality.
•A convolutional sparse coding method for demosaicking panchromatic pixels is proposed•A regularization term to penalize gray filters is added•Colored filters facilitate the reconstruction of color images |
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| AbstractList | Image demosaicking is the problem of reconstructing color images from raw images captured by a digital camera covered by a Color Filter Array (CFA). CFAs allow only part of the incident light to transmit, so they will lose some incident light. Recently, CFAs with panchromatic pixels are used to avoid excessive loss of light. These CFAs’ color is sampled at a sparse set of locations, making demosaicking more challenging. In this paper, we propose a Convolutional Sparse Coding based method for Demosaicking (CSCD) with panchromatic pixels. The CSCD learns a set of filters to decompose training images into color sparse feature maps, then it estimates a set of feature maps of the to-be-reconstructed raw images and gets color images. Convolutional Sparse Coding (CSC) commonly operates on gray images. Naively extending CSC to color images will make the learned filters to be gray, which means that the values of the red, green, and blue channels are almost equal. To address this issue, we add a regularization term to penalize gray filters. We use the Alternating Direction Method of Multipliers (ADMM) to solve the CSCD. We compare the proposed CSCD with the demosaicking methods that are applicable to CFAs with panchromatic pixels. Experimental results validate its advantages in terms of CPSNR and visual quality.
•A convolutional sparse coding method for demosaicking panchromatic pixels is proposed•A regularization term to penalize gray filters is added•Colored filters facilitate the reconstruction of color images Image demosaicking is the problem of reconstructing color images from raw images captured by a digital camera covered by a Color Filter Array (CFA). CFAs allow only part of the incident light to transmit, so they will lose some incident light. Recently, CFAs with panchromatic pixels are used to avoid excessive loss of light. These CFAs' color is sampled at a sparse set of locations, making demosaicking more challenging. In this paper, we propose a Convolutional Sparse Coding based method for Demosaicking (CSCD) with panchromatic pixels. The CSCD learns a set of filters to decompose training images into color sparse feature maps, then it estimates a set of feature maps of the to-be-reconstructed raw images and gets color images. Convolutional Sparse Coding (CSC) commonly operates on gray images. Naively extending CSC to color images will make the learned filters to be gray, which means that the values of the red, green, and blue channels are almost equal. To address this issue, we add a regularization term to penalize gray filters. We use the Alternating Direction Method of Multipliers (ADMM) to solve the CSCD. We compare the proposed CSCD with the demosaicking methods that are applicable to CFAs with panchromatic pixels. Experimental results validate its advantages in terms of CPSNR and visual quality. |
| Author | Bai, Chenyan Li, Jia |
| Author_xml | – sequence: 1 givenname: Chenyan surname: Bai fullname: Bai, Chenyan email: cybai@cnu.edu.cn organization: College of Information Engineering, Capital Normal University, Beijing, PR China – sequence: 2 givenname: Jia surname: Li fullname: Li, Jia organization: Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer Science, Peking University, Beijing, PR China |
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| Keywords | Convolutional sparse coding (CSC) Panchromatic pixels Demosaicking Color filter array (CFA) Alternating direction method of multipliers (ADMM) |
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| SubjectTerms | Alternating direction method of multipliers (ADMM) Color coding Color filter array (CFA) Color imagery Convolutional sparse coding (CSC) Demosaicking Digital cameras Digital imaging Feature maps Image coding Image reconstruction Incident light Panchromatic pixels Pixels Regularization |
| Title | Convolutional sparse coding for demosaicking with panchromatic pixels |
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