Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Removing Chromatic Aberration in Color Image by Gradient Difference Minimization

기울기 차이 최소화를 통한 컬러 영상의 색수차 제거

  • Kwon, Ji Yong (Department of Electrical and Electronic Engineering, Yonsei University) ;
  • Kang, Moon Gi (Department of Electrical and Electronic Engineering, Yonsei University)
  • 권지용 (연세대학교 전기전자공학과) ;
  • 강문기 (연세대학교 전기전자공학과)
  • Received : 2016.08.24
  • Accepted : 2017.01.20
  • Published : 2017.02.25
Download PDF
⟨ Previous Next ⟩

Abstract

Lenses have different refractive indices for different wavelengths of light. This is why different wavelengths of rays are focused at different positions in the focal plane. Images are blurred and noticeable colored edges appear around the objects, which is known as chromatic aberration (CA). In this paper, an algorithm for removing CA artifacts in color images is proposed. Based on the fact that the gradients of color channels are highly correlated, the differences of the gradients of the channels in edges are minimized. The cost function is designed by using the gradients of the channels. Experimental results show the good performance of the proposed algorithm in removing the CA artifacts.

렌즈의 굴절률은 빛의 파장 대역에 따라 다르다. 이로 인하여 서로 다른 파장 대역의 광선들이 다른 위치에서 초점이 맞게 되어 영상의 화질이 떨어지게 되고 에지 주변에서 색수차가 발생하게 된다. 본 논문은 컬러 영상의 색수차를 제거하기 위한 방법을 제안하였다. 컬러 채널들 기울기들의 상관관계가 높다는 이론을 기반으로 하여 컬러 채널들의 기울기 차이에 대한 비용 함수를 설계하였다. 설계된 비용 함수의 에너지를 최소화하도록 하는 해를 찾음으로써 색수차가 제거된 고화질의 컬러 영상을 추정할 수 있다. 추가적으로, 제안하는 방법은 컬러 영상뿐만 아니라 다중 분광 대역 영상에 대해서도 적용 가능하다. 실험 결과에서 제안하는 방법이 효과적으로 색수차를 제거할 수 있다는 것을 보여준다.

Keywords

References

  1. J. Chang, H. Kang, and M. G. Kang, "Correction of axial and lateral chromatic aberration with false color filtering," IEEE Trans. Image Process., vol. 22, no. 3, pp. 1186-1198, Mar. 2013. https://doi.org/10.1109/TIP.2012.2228489
  2. Z. Sadeghipoor, Y. M. Lu, and S. Susstrunk, "Gradient-based correction of chromatic aberration in the joint acquisition of color and near-infrared images," Proc. SPIE 9404, 94040F-94040F-11, 2015.
  3. T. Boult and G. Wolberg, "Correcting chromatic aberrations using image warping," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 684-687, 1992.
  4. B.-K. Kim and R.-H. Park, "Automatic detection and correction of purple fringing using the gradient information and desaturation," in Signal Processing Conference, 2008 16th European, pp. 1-5, 2008.
  5. H. Kang, S.-H. Lee, J. Chang, and M. G. Kang, "Partial differential equation based approach for removal of chromatic aberration with local characteristics," J. Electron. Imaging, vol. 19, no. 3, 2010.
  6. S.-W. Chung, B.-K. Kim, and W.-J. Song, "Removing chromatic aberration by digital image processing," Optical Engineering 49(6), 067002-067002-10, 2010.
  7. F. Sroubek and P. Milanfar, "Robust multichannel blind deconvolution via fast alternating minimization," IEEE Trans. Image Process., vol. 21, no. 4, pp. 1687-1700, Apr. 2012. https://doi.org/10.1109/TIP.2011.2175740
  8. S. H. Chan and T. Q. Nguyen, "Single-image, spatially variant, out-of-focus blur removal," in Proc. IEEE Int. Conf. Image Process., pp. 677-680, Sep. 2011.
  9. D. Menon and G. Calvagno, "Regularization approaches to demosaicking," IEEE Trans. Image Process., vol. 18, no. 10, pp. 2209-2220, Oct. 2009. https://doi.org/10.1109/TIP.2009.2025092
  10. B. Bayer, "Color imaging array," US Patent 3,971,065, 1976.
  11. Du Sic Yoo, Ki Sun Song, and Moon Gi Kang, "A Deblurring Algorithm Combined with Edge Directional Color Demosaicing for Reducing Interpolation Artifacts," Journal of The Institute of Electronics Engineers of Korea, Vol. 50, NO. 7, pp. 1833-1843, July 2013.
  12. C. Schuler, M. Hirsch, S. Harmeling, and B. Scholkopf, "Non-stationary correction of optical aberrations," IEEE International Conference on Computer Vision (ICCV), pp. 659-666, 2011.
  13. J. Y. Kwon and M. G. Kang, "Multispectral demosaicking considering out-of-focus problem for red-green-blue-near-infrared image sensors," J. Electron. Imaging, vol. 25, no. 2, Mar. 2016.
  14. J. Sun, J. Sun, Z. Xu, and H.-Y. Shum, "Gradient profile prior and its applications in image super-resolution and enhancement," IEEE Trans. Image Process., vol. 20, no. 6, pp. 1529-1542, Jun. 2011. https://doi.org/10.1109/TIP.2010.2095871
  15. L. Wang, S. Xiang, G. Meng, H. Wu, and C. Pan, "Edge-directed single-image superresolution via adaptive gradient magnitude self-interpolation," IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 8, pp. 1289-1299, Aug. 2013. https://doi.org/10.1109/TCSVT.2013.2240915
  16. Y.-W. Tai, S. Liu, M. Brown, and S. Lin, Super resolution using edge prior and single image detail synthesis," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2400-2407, 2010.
  17. Q. Yan, Y. Xu, X. Yang, and T. Nguyen, "Single image superresolution based on gradient profile sharpness," IEEE Trans. Image Process., vol. 24, no. 10, pp. 3187-3202, Oct. 2015. https://doi.org/10.1109/TIP.2015.2414877
  18. B. Gunturk, Y. Altunbasak, and R. Mersereau, Color plane interpolation using alternating projections," IEEE Trans. Image Process., vol. 11, no. 9, pp. 997-1013, Sep. 2002. https://doi.org/10.1109/TIP.2002.801121
  19. R. Hardie, K. Barnard, and E. Armstrong, "Joint map registration and high-resolution image estimation using a sequence of undersampled images," IEEE Trans. Image Process., vol. 6, no. 12, pp. 1621-1633, Dec. 1997. https://doi.org/10.1109/83.650116
  20. D. G. Luenberger and Y. Ye, Linear, Nonlinear Programming, Addison-Wesley, 1984.
  21. Sony, "Specifications of imx222lqj," 2013.
  22. I. Pekkucuksen and Y. Altunbasak, "Multiscale Gradients-Based Color Filter Array Interpolation," IEEE Trans. Image Process., vol. 22, no. 1, pp. 157-165, Jan. 2013. https://doi.org/10.1109/TIP.2012.2210726
  23. R. C. Gonzalez and R. E. Woods, Digital image processing, Prentice-Hall, 2006.