CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Deep Learning Approach for Image Denoising and Image Demosaicing

by V. N. V. Satya Prakash, K. Satya Prasad, T. JayaChandra Prasad
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 168 - Number 9
Year of Publication: 2017
Authors: V. N. V. Satya Prakash, K. Satya Prasad, T. JayaChandra Prasad
10.5120/ijca2017914500

V. N. V. Satya Prakash, K. Satya Prasad, T. JayaChandra Prasad . Deep Learning Approach for Image Denoising and Image Demosaicing. International Journal of Computer Applications. 168, 9 ( Jun 2017), 18-26. DOI=10.5120/ijca2017914500

@article{ 10.5120/ijca2017914500,
author = { V. N. V. Satya Prakash, K. Satya Prasad, T. JayaChandra Prasad },
title = { Deep Learning Approach for Image Denoising and Image Demosaicing },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 168 },
number = { 9 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 18-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume168/number9/27902-2017914500/ },
doi = { 10.5120/ijca2017914500 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:15:40.690749+05:30
%A V. N. V. Satya Prakash
%A K. Satya Prasad
%A T. JayaChandra Prasad
%T Deep Learning Approach for Image Denoising and Image Demosaicing
%J International Journal of Computer Applications
%@ 0975-8887
%V 168
%N 9
%P 18-26
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Color image normally contain of three main colors at the each pixel, but the digital cameras capture only one color at each pixel using color filter array (CFA). While through capturing in color image, some noise/artifacts is added. So, the both demosaicing and de-noising are the first essential task in digital camera. Here, both the technique can be solve sequentially and independently. A conventional neural network based de-noising technique has applied for the removal of noise/artifacts. Afterwards, frequency based demosaicing with the convolutional neural network based image reconstruction algorithm is apply to acquire another two missing color component. The result analysis presented in this paper demonstrate that our proposed de-nosing and demosaicing exhibits the better performance and it is applicable for a large variety of images.

References
  1. Lu, Y.M.; Karzand, M.; Vetterli, M., "Demosaicking by Alternating Projections: Theory and Fast One-Step Implementation," in IEEE Transactions on Image Processing , vol.19, no.8, pp.2085-2098, Aug. 2010
  2. Z. Zha, X. Liu, X. Huang, X. Hong, H. Shi, Y. Xu, Q. Wang, L. Tang, and X. Zhang, “Analyzing the group sparsity based on the rank minimization methods,” arXiv preprint arXiv:1611.08983, 2016
  3. L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D: Nonlinear Phenomena, vol. 60, no. 1, pp. 259–268, 1992
  4. S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation-based image restoration,” Multiscale Modeling & Simulation, vol. 4, no. 2, pp. 460–489, 2005.
  5. J. Xu, L. Zhang, W. Zuo, D. Zhang, and X. Feng, “Patch group based nonlocal self-similarity prior learning for image denoising,” in International Conference on Computer Vision, 2015, pp. 244–252.
  6. W. Dong, L. Zhang, G. Shi, and X. Li, “Nonlocally centralized sparse representation for image restoration,” IEEE Transactions on Image Processing, vol. 22, no. 4, pp. 1620–1630, 2013.
  7. A.Buades, B. Coll, and J.-M. Morel, “Nonlocal image and movie denoising,” International Journal of Computer Vision, vol. 76, no. 2, pp. 123–139, 2008.
  8. Y. Weiss and W. T. Freeman, “What makes a good model of natural images?” in IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp. 1–8
  9. X. Lan, S. Roth, D. Huttenlocher, and M. J. Black, “Efficient belief propagation with learned higher-order Markov random fields,” in European Conference on Computer Vision, 2006, pp. 269–282.
  10. Ousman Boukara,Laurent Bitjokaa and Gamraïkréo Djaowéa. “Nondestructive Determination Of Beans Water Absorption Capacity Using CFA Images Analysis For Hard-To-Cook Evaluation”. International Journal of Electrical and Computer Engineering (IJECE).Vol. 3, No. 3, June 2013, pp. 317~328
  11. Nai-Xiang Lian; Lanlan Chang; Yap-Peng Tan; Zagorodnov, V., "Adaptive Filtering for Color Filter Array Demosaicking," in IEEE Transactions on Image Processing , vol.16, no.10, pp.2515-2525, Oct. 2007
  12. Xiao Zhou; Fanfan Yang; Chunxiao Zhang; Chengyou Wang, "Improved adaptive demosaicking using directional weighting," in Computer Science & Education (ICCSE), 2014 9th International Conference on , vol., no., pp.615-618, 22-24 Aug. 2014
  13. Zhang, D.; Xiaolin Wu, "Color demosaicking via directional linear minimum mean square-error estimation," in IEEE Transactions on Image Processing , vol.14, no.12, pp.2167-2178, Dec. 2005
  14. Kim, Y.; Jeong, J., "Four Direction Residual Interpolation for Demosaicking," in IEEE Transactions on Circuits and Systems for Video Technology, vol.PP, no.99, pp.1-1. doi: 10.1109/TCSVT.2015.2428552
  15. Maschal, R.A.; Young, S.S.; Reynolds, J.P.; Krapels, K.; Fanning, J.; Corbin, T., "New Image Quality Assessment Algorithms for CFA Demosaicing," in IEEE Sensors Journal , vol.13, no.1, pp.371-378, Jan. 2013
  16. Xiangdong Chen; Liwen He; Gwanggil Jeon; Jechang Jeong, "Multidirectional Weighted Interpolation and Refinement Method for Bayer Pattern CFA Demosaicking," in IEEE Transactions on Circuits and Systems for Video Technology, vol.25, no.8, pp.1271-1282, Aug. 2015
  17. R.Niruban, T.Sree Renga Raja and R.Deepa. “Similarity and Variance of Color Difference Based Demosaicing.” TELKOMNIKA Indonesian Journal of Electrical Engineering. Vol. 13, No. 2, February 2015, pp. 238-246
  18. Michael, G.; Kiryati, N., "Example based demosaicing," in 2014 IEEE International Conference on Image Processing (ICIP), vol., no., pp.1832-1836, 27-30 Oct. 2014
  19. Lu, Y.M.; Karzand, M.; Vetterli, M., "Demosaicking by Alternating Projections: Theory and Fast One-Step Implementation," in IEEE Transactions on Image Processing, vol.19, no.8, pp.2085-2098, Aug. 2010
  20. Wei Ye; Kai-Kuang Ma, "Color Image Demosaicing Using Iterative Residual Interpolation," in IEEE Transactions on Image Processing, , vol.24, no.12, pp.5879-5891, Dec. 2015
  21. D.H. Brainard and D. Sherman,” Reconstructing images from trichromatic samples: From basic research to practical applications”. In Proc. of the IS&T /SID, 1995.
  22. D. Keren. “An adaptive bayesian approach to demosaicing color images.” Technical Report HPL-96-129, Hewlett-Packard, 1996.
  23. K.-H. Chung and Y.-H. Chan. “Color demosaicing using variance of color differences.” IEEE Trans. on Image Processing 15:2944-2955, 2006.
  24. R. Sher and M. Porat. “CCD Image Demosaicing using Localized Correlations.” In Proc. of EUSIPCO, Poznan, Poland, Sept. 2007.
  25. R. Kimmel,”Demosaicing: Image reconstruction from color CCD samples.” IEEE Trans. on Image Processing 8:1221-1228, 1999.
  26. M. R. Gupta and T. Chen.” Vector color filter array demosaicing.” in Proc. of SPIE, Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications II 4306:374-382, 2001.
  27. E. Gershikov. “Optimized Color Transforms for Image Demosaicing” International Journal Of Computational Engineering Research (ijceronline.com) Vol. 3 Issue. 3. Mar 2013
  28. D. Kiku, Y. Monno, M. Tanaka, and M. Okutomi, “Minimized-Laplacian residual interpolation for color image demosaicking,” Proc. of SPIE, vol. 9023, pp. 90230L–1–8, 2014.
  29. W. Ye and K. K. Ma, “Image demosaicing by using iterative residual interpolation,” Proc. of IEEE Int. Conf. on Image Processing (ICIP), pp. 1862–1866, 2014.
  30. Y. Monno and D. Kiku. “Adaptive residual interpolation for color image demosaicking” in IEEE Transactions on Image Processing ,pp.3861-3865,2015
  31. Y. Monno, D. Kiku, S. Kikuchi, M. Tanaka, and M. Okutomi, “Multispectral demosaicking with novel guide image generation and residual interpolation,” Proc. of IEEE Int. Conf. on Image Processing (ICIP), pp. 645–649, 2014
  32. T. Yu, W. Hu, W. Xue and W. Zhang “Colour image demosaicking via joint intra and inter channel information” electronics letters Vol. 52 No. 8 pp. 605–607. 14th April 2016.
  33. Jiqing Wu, Radu Timofte, “Demosaicing based on Directional Difference Regression and Efficient Regression Priors” in IEEE Transactions on image Processing, 2016
  34. Daisuke Kiku, Yusuke Monno, “Beyond Color Difference: Residual Interpolation for Color Image Demosaicking” IEEE Transactions on Image Processing, vol. 25, no. 3, March 2016.
  35. I.Pekkucuksen and Y. Altunbasak, “Gradient based threshold free color filter array interpolation,” in Proc. of IEEE Int. Conf. Image Process. (ICIP), Sep. 2010, pp. 137–140.
  36. K. Hirakawa and P. J. Wolfe, “Spatio-spectral color filter array design for optimal image recovery,” in IEEE Transactions on Image Processing., vol. 17, no. 10, pp. 1876–1890, Oct. 2008
  37. L. Condat, “A new color filter array with optimal properties for noiseless and noisy color image acquisition,” in IEEE Transactions on Image Processing., vol. 20, no. 8, pp. 2200–2210, Aug. 2011.
  38. Alleysson, D.; Susstrunk, S.; Herault, J., "Linear demosaicing inspired by the human visual system," in IEEE Transactions on Image Processing , vol.14, no.4, pp.439-449, April 2005
  39. T. Q. Pham, L. J. van Vliet, and K. Schutte, “Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution,” EURASIP Journal on Applied Signal Processing 2006, 2006. Article ID 83268, 12 pages.
  40. D. Robinson and P. Milanfar, “Statistical performance analysis of super-resolution,” IEEE Transactions on Image Processing 15, pp. 1413–1428, June 2006
  41. Q.V. Le, A. Coates, B. Prochnow, and A.Y. Ng. ,”On optimization methods for deep learning.”Learning, pages 265–272, 2011.
  42. Jia Li, Chenyan Bai, "Automatic Design of High-Sensitivity Color Filter Arrays with Panchromatic Pixels" in IEEE Transactions on Image Processing, 2017
  43. J. Wang, C. Zhang, and P. Hao, “New color filter arrays of high light sensitivity and high demosaicking performance,” in Proceedings of IEEE International Conference on Image Processing. IEEE, 2011, pp. 3153–3156
  44. L. Wang and G. Jeon, "Bayer Pattern CFA Demosaicking Based on Multi-Directional Weighted Interpolation and Guided Filter," in IEEE Signal Processing Letters, vol. 22, no. 11, pp. 2083-2087, Nov. 2015
Index Terms

Computer Science
Information Sciences

Keywords

Demosaicing Color image Color filter array (CFA) Digital camera Conventional neural network (CNN)

Powered by PhDFocusTM