Abstract
Upsampling with preserving image details is highly demanded image operation. There are various upsampling algorithms. Many upsampling algorithms focus on the gray image. For color images, those algorithms are usually applied to a luminance component only, or independently applied channel by channel. However, we can not observe the full-color image by a single image sensor equipped in a common digital camera. The data observed by the single image sensor is called raw data. The raw data is converted into the full-color image by demosaicing. Upsampling from the raw data requires sequential processes of demosaicing and upsampling. In this paper, we propose direct upsampling from the raw data based on a kernel regression. Although the kernel regression is known as powerful denoising and interpolation algorithm, the kernel regression has been also proposed for the gray image. We extend to the color kernel regression which can generate the full-color image from any kind of raw data. Second key point of the proposed color kernel regression is a local density parameter optimization, or kernel size optimization, based on the stability of the linear system associated to the kernel regression. We also propose a novel iteration framework for the upsampling. The experimental results demonstrate that the proposed color kernel regression outperforms existing sequential approaches, reconstruction approaches, and existing kernel regression.
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References
Takeda, H., Farsiu, S., Milanfar, P.: Kernel Regression for Image Processing and Reconstruction. IEEE Transactions on Image Processing 16, 349–366 (2007)
Fattal, R.: Image upsampling via imposed edge statistics. ACM Transactions on Graphics (TOG) (26)
Shan, Q., Li, Z., Jia, J., Tang, C.: Fast image/video upsampling. In: ACM SIGGRAPH Asia 2008 papers, pp. 1–7. ACM, New York (2008)
Freeman, W., Jones, T., Pasztor, E.: Example-based super-resolution. IEEE Computer Graphics and Applications, 56–65 (2002)
Glasner, D., Bagon, S., Irani, M.: Super-Resolution from a Single Image. In: IEEE International Conference on Conputer Vision (ICCV) (2009)
Zhang, X., Wu, X.: Image Interpolation by Adaptive 2D Autoregressive Modeling and Soft-Decision Estimation. IEEE Transactions on Image Processing 17, 887–896 (2008)
Kopf, J., Cohen, M., Lischinski, D., Uyttendaele, M.: Joint bilateral upsampling. ACM Transactions on Graphics (26)
Bayer, B.: Color imaging array (1976)
Li, X., Gunturk, B., Zhang, L.: Image demosaicing: A systematic survey (Visual Communications and Image Processing) (2008)
Gunturk, B., Glotzbach, J., Altunbasak, Y., Schafer, R., Mersereau, R.: Demosaicking: color filter array interpolation. IEEE Signal Processing Magazine 22, 44–54 (2005)
Wu, X., Zhang, N.: Primary-consistent soft-decision color demosaicking for digital cameras. IEEE Transactions on image processing 13, 1263–1274 (2004)
Hirakawa, K., Parks, T.: Adaptive homogeneity-directed demosaicing algorithm. IEEE Transactions on Image Processing 14, 360–369 (2005)
Gotoh, T., Okutomi, M.: Direct super-resolution and registration using raw CFA images. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 600–607 (2004)
Farsiu, S., Elad, M., Milanfar, P.: Multiframe demosaicing and super-resolution of color images. IEEE Transactions on Image Processing 15, 141–159 (2006)
Levin, A., Fergus, R., Durand, F., Freeman, W.: Image and depth from a conventional camera with a coded aperture. ACM Transactions on Graphics 26, 70 (2007)
Nadaraya, E.: On estimating regression. Theory of Probability and its Applications 9, 141 (1964)
Silverman, B.: Density estimation for statistics and data analysis (1986)
Chatterjee, P., Milanfar, P.: A generalization of non-local means via kernel regression. In: Proc. of SPIE Conf. on Computational Imaging (2008)
Dabov, K., Foi, A., Egiazarian, K.: Image restoration by sparse 3D transform-domain collaborative filtering. In: Proc. SPIE Electronic Imaging (2008)
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Tanaka, M., Okutomi, M. (2011). Color Kernel Regression for Robust Direct Upsampling from Raw Data of General Color Filter Array. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_23
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DOI: https://doi.org/10.1007/978-3-642-19318-7_23
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