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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| Published in: | Signal processing. Image communication Vol. 77; pp. 20 - 27 |
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| Language: | English |
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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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| Cites_doi | 10.1016/j.image.2011.04.003 10.1109/MSP.2005.1407714 10.1109/TIP.2018.2803341 10.1109/TIP.2017.2679440 10.1109/TIP.2015.2495260 10.1109/TCE.2005.1561853 10.1007/s10994-014-5469-5 10.1109/TED.2009.2030630 10.1109/TIP.2016.2633869 10.1109/LSP.2005.859503 10.1109/TIP.2008.2002164 10.1109/TIP.2011.2107524 10.1016/j.neucom.2011.12.025 10.1109/TIP.2015.2409569 10.1117/1.JEI.27.4.043047 10.1109/TIP.2004.838691 10.1109/TIP.2007.911828 10.1109/TIP.2018.2842152 10.1109/TIP.2003.816004 10.1109/TIP.2010.2077642 10.1109/TIP.2013.2244215 10.1109/TIP.2017.2781303 |
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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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| References | Li, Gunturk, Zhang (b8) 2008 Bai, Li, Lin, Yu, Chen (b11) 2015; 24 Tan, Chen, Hua (b26) 2018; 27 Hu, Heide, Dai, Wetzstein (b37) 2018; 27 Leung, Jeon, Dubois (b22) 2011; 20 Zeiler, Krishnan, Taylor, Fergus (b29) 2010 Zhang, Tao (b25) 2014 Gu, Zuo, Xie, Meng (b35) 2015 L. Shi, B. Funt, Re-processed version of the gehler color constancy dataset of 568 images, Simon Fraser University. Kan, Ding, Li (b12) 2015; 39 Kiku, Monno, Tanaka, Okutomi (b23) 2013 Gehler, Rother, Blake, Minka, Sharp (b42) 2008 Rehman, Shao (b10) 2012; 83 Kokkinos, Lefkimmiatis (b27) 2018 Hirakawa, Parks (b47) 2005; 14 Menon, Calvagno (b9) 2011; 26 Least-Squares Luma-Chroma Demultiplexing Algorithm, URL Gunturk, Glotzbach, Altunbasak, Schafer, Mersereau (b7) 2005; 22 Lu, Tan (b46) 2003; 12 B.E. Bayer, Color imaging array, U.S. Patent 3,971,065, 1976. Hirakawa, Wolfe (b3) 2008; 17 Choudhury, Swanson, Heide, Wetzstein, Heidrich (b32) 2017 Bai, Li, Lin (b28) 2018; 27 Kumar, Morales, Adams Jr, Hao (b14) 2009 2018. Bristow, Eriksson, Lucey (b30) 2013 2011. J. Wang, L. Zhao, L. Wu, Multi-convex Inequality-constrained Alternating Direction Method of Multipliers Dubois (b19) 2005; 12 Rethinking Color Cameras, URL Heide, Heidrich, Wetzstein (b31) 2015 J.T. Compton, J.F. Hamilton Jr, Image sensor with improved light sensitivity, U.S. Patent 20,070,024,931, 2017. Lin, Liu, Li (b39) 2015; 99 Honda, Iida, Egawa, Seki (b15) 2009; 56 2015. Wohlberg (b36) 2015; 25 2019. Li, Bai, Lin, Yu (b13) 2017; 26 Moghadam, Aghagolzadeh, Kumar, Radha (b24) 2013; 22 Lukac, Plataniotis (b2) 2005; 51 Hao, Li, Lin, Dubois (b4) 2011; 20 Cai, Candès, Shen (b40) 2010; 20 Papyan, Romano, Elad (b33) 2017; 18 Li, Bai, Lin, Yu (b6) 2017; 26 Wang, Yao, Kwok, Ni (b34) 2018; 27 Chakrabarti, Freeman, Zickler (b16) 2014 Bai, Li, Lin, Yu (b5) 2016; 25 Mairal, Bach, Ponce, Sapiro, Zisserman (b21) 2009 Mairal, Elad, Sapiro (b20) 2008; 17 Peak Signal to Noise Ratio, URL Wang, Zhang, Hao (b18) 2011 Li (10.1016/j.image.2019.05.018_b13) 2017; 26 Wang (10.1016/j.image.2019.05.018_b18) 2011 Cai (10.1016/j.image.2019.05.018_b40) 2010; 20 Gehler (10.1016/j.image.2019.05.018_b42) 2008 Bai (10.1016/j.image.2019.05.018_b28) 2018; 27 Heide (10.1016/j.image.2019.05.018_b31) 2015 Gu (10.1016/j.image.2019.05.018_b35) 2015 Mairal (10.1016/j.image.2019.05.018_b20) 2008; 17 Kokkinos (10.1016/j.image.2019.05.018_b27) 2018 Papyan (10.1016/j.image.2019.05.018_b33) 2017; 18 10.1016/j.image.2019.05.018_b17 Hao (10.1016/j.image.2019.05.018_b4) 2011; 20 10.1016/j.image.2019.05.018_b38 Zhang (10.1016/j.image.2019.05.018_b25) 2014 Honda (10.1016/j.image.2019.05.018_b15) 2009; 56 Dubois (10.1016/j.image.2019.05.018_b19) 2005; 12 Kiku (10.1016/j.image.2019.05.018_b23) 2013 Bristow (10.1016/j.image.2019.05.018_b30) 2013 Rehman (10.1016/j.image.2019.05.018_b10) 2012; 83 Hu (10.1016/j.image.2019.05.018_b37) 2018; 27 Lu (10.1016/j.image.2019.05.018_b46) 2003; 12 Li (10.1016/j.image.2019.05.018_b6) 2017; 26 Chakrabarti (10.1016/j.image.2019.05.018_b16) 2014 Wohlberg (10.1016/j.image.2019.05.018_b36) 2015; 25 Hirakawa (10.1016/j.image.2019.05.018_b47) 2005; 14 Choudhury (10.1016/j.image.2019.05.018_b32) 2017 Wang (10.1016/j.image.2019.05.018_b34) 2018; 27 Lin (10.1016/j.image.2019.05.018_b39) 2015; 99 Menon (10.1016/j.image.2019.05.018_b9) 2011; 26 Bai (10.1016/j.image.2019.05.018_b11) 2015; 24 Kan (10.1016/j.image.2019.05.018_b12) 2015; 39 Lukac (10.1016/j.image.2019.05.018_b2) 2005; 51 Mairal (10.1016/j.image.2019.05.018_b21) 2009 Moghadam (10.1016/j.image.2019.05.018_b24) 2013; 22 Gunturk (10.1016/j.image.2019.05.018_b7) 2005; 22 Bai (10.1016/j.image.2019.05.018_b5) 2016; 25 10.1016/j.image.2019.05.018_b44 10.1016/j.image.2019.05.018_b1 Hirakawa (10.1016/j.image.2019.05.018_b3) 2008; 17 Kumar (10.1016/j.image.2019.05.018_b14) 2009 Tan (10.1016/j.image.2019.05.018_b26) 2018; 27 10.1016/j.image.2019.05.018_b45 10.1016/j.image.2019.05.018_b43 Zeiler (10.1016/j.image.2019.05.018_b29) 2010 10.1016/j.image.2019.05.018_b41 Li (10.1016/j.image.2019.05.018_b8) 2008 Leung (10.1016/j.image.2019.05.018_b22) 2011; 20 |
| References_xml | – volume: 25 start-page: 1793 year: 2016 end-page: 1807 ident: b5 article-title: Automatic design of color filter arrays in the frequency domain publication-title: IEEE Trans. Image Process. – reference: , 2019. – reference: J.T. Compton, J.F. Hamilton Jr, Image sensor with improved light sensitivity, U.S. Patent 20,070,024,931, 2017. – start-page: 1 year: 2008 end-page: 8 ident: b42 article-title: Bayesian Color constancy revisited publication-title: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition – volume: 17 start-page: 53 year: 2008 end-page: 69 ident: b20 article-title: Sparse representation for color image restoration publication-title: IEEE Trans. Image Process. – volume: 83 start-page: 222 year: 2012 end-page: 228 ident: b10 article-title: Classification-based de-mosaicing for digital cameras publication-title: Neurocomputing – start-page: 303 year: 2018 end-page: 319 ident: b27 article-title: Deep image demosaicking using a Cascade of convolutional residual denoising networks publication-title: Proceedings of the European Conference on Computer Vision – reference: Peak Signal to Noise Ratio, URL – volume: 12 start-page: 847 year: 2005 end-page: 850 ident: b19 article-title: Frequency-domain methods for demosaicking of bayer-sampled color images publication-title: IEEE Signal Process. Lett. – volume: 27 start-page: 1611 year: 2018 end-page: 1625 ident: b37 article-title: Convolutional sparse coding for rgb+nir imaging publication-title: IEEE Trans. Image Process. – reference: Least-Squares Luma-Chroma Demultiplexing Algorithm, URL – reference: , 2015. – start-page: 5135 year: 2015 end-page: 5143 ident: b31 article-title: Fast and flexible convolutional sparse coding publication-title: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition – volume: 51 start-page: 1260 year: 2005 end-page: 1267 ident: b2 article-title: Color filter arrays: design and performance analysis publication-title: IEEE Trans. Consum. Electron. – volume: 20 start-page: 709 year: 2011 end-page: 722 ident: b4 article-title: A geometric method for optimal design of color filter arrays publication-title: IEEE Trans. Image Process. – volume: 27 start-page: 2408 year: 2018 end-page: 2419 ident: b26 article-title: Deepdemosaicking: Adaptive image demosaicking via multiple deep fully convolutional networks publication-title: IEEE Trans. Image Process. – start-page: 2304 year: 2013 end-page: 2308 ident: b23 article-title: Residual interpolation for color image demosaicking publication-title: IEEE International Conference on Image Processing – start-page: 1823 year: 2015 end-page: 1831 ident: b35 article-title: Convolutional sparse coding for image super-resolution publication-title: Proceedings of the IEEE International Conference on Computer Vision – reference: Rethinking Color Cameras, URL – volume: 99 start-page: 287 year: 2015 end-page: 325 ident: b39 article-title: Linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning publication-title: Mach. Learn. – start-page: 4290 year: 2017 end-page: 4298 ident: b32 article-title: Consensus convolutional sparse coding publication-title: Proceedings of IEEE International Conference on Computer Vision – volume: 24 start-page: 1672 year: 2015 end-page: 1684 ident: b11 article-title: Penrose demosaicking publication-title: IEEE Trans. Image Process. – start-page: 391 year: 2013 end-page: 398 ident: b30 article-title: Fast convolutional sparse coding publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 26 start-page: 870 year: 2017 end-page: 883 ident: b6 article-title: Automatic design of high-sensitivity color filter arrays with panchromatic pixels publication-title: IEEE Trans. Image Process. – start-page: 236 year: 2014 end-page: 251 ident: b25 article-title: A patch aware multiple dictionary framework for demosaicing publication-title: Proceedings of Asian Conference on Computer Vision – volume: 39 start-page: 264 year: 2015 end-page: 279 ident: b12 article-title: Color image demosaicking using inter-channel correlation and nonlocal self-similarity publication-title: Signal Process., Image Commun. – volume: 17 start-page: 1876 year: 2008 end-page: 1890 ident: b3 article-title: Spatio-spectral color filter array design for optimal image recovery publication-title: IEEE Trans. Image Process. – reference: , 2011. – volume: 12 start-page: 1194 year: 2003 end-page: 1210 ident: b46 article-title: Color filter array demosaicking: new method and performance measures publication-title: IEEE Trans. Image Process. – volume: 26 start-page: 518 year: 2011 end-page: 533 ident: b9 article-title: Color image demosaicking: an overview publication-title: Signal Process., Image Commun. – volume: 14 start-page: 360 year: 2005 end-page: 369 ident: b47 article-title: Adaptive homogeneity-directed demosaicing algorithm publication-title: IEEE Trans. Image Process. – volume: 22 start-page: 44 year: 2005 end-page: 54 ident: b7 article-title: Demosaicking: color filter array interpolation publication-title: IEEE Signal Process. Mag. – reference: J. Wang, L. Zhao, L. Wu, Multi-convex Inequality-constrained Alternating Direction Method of Multipliers, – start-page: 2681 year: 2009 end-page: 2684 ident: b14 article-title: New digital camera sensor architecture for low light imaging publication-title: Proceedings of IEEE International Conference on Image Processing – start-page: 3153 year: 2011 end-page: 3156 ident: b18 article-title: New color filter arrays of high light sensitivity and high demosaicking performance publication-title: Proceedings of IEEE International Conference on Image Processing – start-page: 2528 year: 2010 end-page: 2535 ident: b29 article-title: Deconvolutional networks publication-title: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition – volume: 25 start-page: 301 year: 2015 end-page: 315 ident: b36 article-title: Efficient algorithms for convolutional sparse representations publication-title: IEEE Trans. Image Process. – volume: 26 start-page: 2381 year: 2017 end-page: 2393 ident: b13 article-title: Optimized color filter arrays for sparse representation-based demosaicking publication-title: IEEE Trans. Image Process. – start-page: 1 year: 2014 end-page: 8 ident: b16 article-title: Rethinking color cameras publication-title: Proceedings of IEEE International Conference on Computational Photography – volume: 20 start-page: 1885 year: 2011 end-page: 1894 ident: b22 article-title: Least-squares luma–chroma demultiplexing algorithm for bayer demosaicking publication-title: IEEE Trans. Image Process. – volume: 20 start-page: 1956 year: 2010 end-page: 1982 ident: b40 article-title: A singular value thresholding algorithm for matrix completion publication-title: J. Optim. – reference: , 2018. – volume: 18 start-page: 1 year: 2017 end-page: 52 ident: b33 article-title: Convolutional neural networks analyzed via convolutional sparse coding publication-title: J. Mach. Learn. Res. – volume: 27 start-page: 043047 year: 2018 end-page: 1–043047–9 ident: b28 article-title: Channel-correlation adaptive dictionary learning based demosaicking publication-title: J. Electron. Imaging – reference: L. Shi, B. Funt, Re-processed version of the gehler color constancy dataset of 568 images, Simon Fraser University. – volume: 56 start-page: 2398 year: 2009 end-page: 2402 ident: b15 article-title: A color CMOS imager with 4x4 white-RGB color filter array for increased low-illumination signal-to-noise ratio publication-title: IEEE Trans. Electron Devices – reference: B.E. Bayer, Color imaging array, U.S. Patent 3,971,065, 1976. – volume: 22 start-page: 2356 year: 2013 end-page: 2371 ident: b24 article-title: Compressive framework for demosaicing of natural images publication-title: IEEE Trans. Image Process. – start-page: 2272 year: 2009 end-page: 2279 ident: b21 article-title: Non-local sparse models for image restoration publication-title: Proceedings of IEEE International Conference on Computer Vision – year: 2008 ident: b8 article-title: Image demosaicing: a systematic survey publication-title: Proceedings of SPIE on Visual Communications and Image Processing, Vol. 6822 – volume: 27 start-page: 4850 year: 2018 end-page: 4859 ident: b34 article-title: Scalable online convolutional sparse coding publication-title: IEEE Trans. Image Process. – volume: 25 start-page: 1793 issue: 4 year: 2016 ident: 10.1016/j.image.2019.05.018_b5 article-title: Automatic design of color filter arrays in the frequency domain publication-title: IEEE Trans. Image Process. – volume: 26 start-page: 518 issue: 8 year: 2011 ident: 10.1016/j.image.2019.05.018_b9 article-title: Color image demosaicking: an overview publication-title: Signal Process., Image Commun. doi: 10.1016/j.image.2011.04.003 – volume: 22 start-page: 44 issue: 1 year: 2005 ident: 10.1016/j.image.2019.05.018_b7 article-title: Demosaicking: color filter array interpolation publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2005.1407714 – start-page: 2681 year: 2009 ident: 10.1016/j.image.2019.05.018_b14 article-title: New digital camera sensor architecture for low light imaging – start-page: 2304 year: 2013 ident: 10.1016/j.image.2019.05.018_b23 article-title: Residual interpolation for color image demosaicking – start-page: 391 year: 2013 ident: 10.1016/j.image.2019.05.018_b30 article-title: Fast convolutional sparse coding – volume: 27 start-page: 2408 issue: 5 year: 2018 ident: 10.1016/j.image.2019.05.018_b26 article-title: Deepdemosaicking: Adaptive image demosaicking via multiple deep fully convolutional networks publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2018.2803341 – ident: 10.1016/j.image.2019.05.018_b44 – start-page: 2272 year: 2009 ident: 10.1016/j.image.2019.05.018_b21 article-title: Non-local sparse models for image restoration – volume: 26 start-page: 2381 issue: 5 year: 2017 ident: 10.1016/j.image.2019.05.018_b13 article-title: Optimized color filter arrays for sparse representation-based demosaicking publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2679440 – volume: 25 start-page: 301 issue: 1 year: 2015 ident: 10.1016/j.image.2019.05.018_b36 article-title: Efficient algorithms for convolutional sparse representations publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2015.2495260 – volume: 18 start-page: 1 issue: 83 year: 2017 ident: 10.1016/j.image.2019.05.018_b33 article-title: Convolutional neural networks analyzed via convolutional sparse coding publication-title: J. Mach. Learn. Res. – volume: 51 start-page: 1260 issue: 4 year: 2005 ident: 10.1016/j.image.2019.05.018_b2 article-title: Color filter arrays: design and performance analysis publication-title: IEEE Trans. Consum. Electron. doi: 10.1109/TCE.2005.1561853 – start-page: 1823 year: 2015 ident: 10.1016/j.image.2019.05.018_b35 article-title: Convolutional sparse coding for image super-resolution – volume: 99 start-page: 287 issue: 2 year: 2015 ident: 10.1016/j.image.2019.05.018_b39 article-title: Linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning publication-title: Mach. Learn. doi: 10.1007/s10994-014-5469-5 – volume: 56 start-page: 2398 issue: 11 year: 2009 ident: 10.1016/j.image.2019.05.018_b15 article-title: A color CMOS imager with 4x4 white-RGB color filter array for increased low-illumination signal-to-noise ratio publication-title: IEEE Trans. Electron Devices doi: 10.1109/TED.2009.2030630 – start-page: 236 year: 2014 ident: 10.1016/j.image.2019.05.018_b25 article-title: A patch aware multiple dictionary framework for demosaicing – volume: 26 start-page: 870 issue: 2 year: 2017 ident: 10.1016/j.image.2019.05.018_b6 article-title: Automatic design of high-sensitivity color filter arrays with panchromatic pixels publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2016.2633869 – volume: 12 start-page: 847 issue: 12 year: 2005 ident: 10.1016/j.image.2019.05.018_b19 article-title: Frequency-domain methods for demosaicking of bayer-sampled color images publication-title: IEEE Signal Process. Lett. doi: 10.1109/LSP.2005.859503 – volume: 17 start-page: 1876 issue: 10 year: 2008 ident: 10.1016/j.image.2019.05.018_b3 article-title: Spatio-spectral color filter array design for optimal image recovery publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2008.2002164 – volume: 39 start-page: 264 issue: Part A year: 2015 ident: 10.1016/j.image.2019.05.018_b12 article-title: Color image demosaicking using inter-channel correlation and nonlocal self-similarity publication-title: Signal Process., Image Commun. – volume: 20 start-page: 1885 issue: 7 year: 2011 ident: 10.1016/j.image.2019.05.018_b22 article-title: Least-squares luma–chroma demultiplexing algorithm for bayer demosaicking publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2011.2107524 – volume: 83 start-page: 222 issue: 6 year: 2012 ident: 10.1016/j.image.2019.05.018_b10 article-title: Classification-based de-mosaicing for digital cameras publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.12.025 – start-page: 2528 year: 2010 ident: 10.1016/j.image.2019.05.018_b29 article-title: Deconvolutional networks – volume: 24 start-page: 1672 issue: 5 year: 2015 ident: 10.1016/j.image.2019.05.018_b11 article-title: Penrose demosaicking publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2015.2409569 – ident: 10.1016/j.image.2019.05.018_b45 – volume: 27 start-page: 043047 issue: 4 year: 2018 ident: 10.1016/j.image.2019.05.018_b28 article-title: Channel-correlation adaptive dictionary learning based demosaicking publication-title: J. Electron. Imaging doi: 10.1117/1.JEI.27.4.043047 – volume: 14 start-page: 360 issue: 3 year: 2005 ident: 10.1016/j.image.2019.05.018_b47 article-title: Adaptive homogeneity-directed demosaicing algorithm publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2004.838691 – start-page: 5135 year: 2015 ident: 10.1016/j.image.2019.05.018_b31 article-title: Fast and flexible convolutional sparse coding – start-page: 1 year: 2014 ident: 10.1016/j.image.2019.05.018_b16 article-title: Rethinking color cameras – volume: 17 start-page: 53 issue: 1 year: 2008 ident: 10.1016/j.image.2019.05.018_b20 article-title: Sparse representation for color image restoration publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2007.911828 – volume: 20 start-page: 1956 issue: 4 year: 2010 ident: 10.1016/j.image.2019.05.018_b40 article-title: A singular value thresholding algorithm for matrix completion publication-title: J. Optim. – volume: 27 start-page: 4850 issue: 10 year: 2018 ident: 10.1016/j.image.2019.05.018_b34 article-title: Scalable online convolutional sparse coding publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2018.2842152 – ident: 10.1016/j.image.2019.05.018_b38 – volume: 12 start-page: 1194 issue: 10 year: 2003 ident: 10.1016/j.image.2019.05.018_b46 article-title: Color filter array demosaicking: new method and performance measures publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2003.816004 – ident: 10.1016/j.image.2019.05.018_b1 – volume: 20 start-page: 709 issue: 3 year: 2011 ident: 10.1016/j.image.2019.05.018_b4 article-title: A geometric method for optimal design of color filter arrays publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2010.2077642 – ident: 10.1016/j.image.2019.05.018_b17 – ident: 10.1016/j.image.2019.05.018_b43 – start-page: 303 year: 2018 ident: 10.1016/j.image.2019.05.018_b27 article-title: Deep image demosaicking using a Cascade of convolutional residual denoising networks – start-page: 4290 year: 2017 ident: 10.1016/j.image.2019.05.018_b32 article-title: Consensus convolutional sparse coding – ident: 10.1016/j.image.2019.05.018_b41 – volume: 22 start-page: 2356 issue: 6 year: 2013 ident: 10.1016/j.image.2019.05.018_b24 article-title: Compressive framework for demosaicing of natural images publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2013.2244215 – start-page: 1 year: 2008 ident: 10.1016/j.image.2019.05.018_b42 article-title: Bayesian Color constancy revisited – volume: 27 start-page: 1611 issue: 4 year: 2018 ident: 10.1016/j.image.2019.05.018_b37 article-title: Convolutional sparse coding for rgb+nir imaging publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2781303 – year: 2008 ident: 10.1016/j.image.2019.05.018_b8 article-title: Image demosaicing: a systematic survey – start-page: 3153 year: 2011 ident: 10.1016/j.image.2019.05.018_b18 article-title: New color filter arrays of high light sensitivity and high demosaicking performance |
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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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