Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

Adaptive Unsharp Masking Filter Design Based on Multi-Scale Retinex for Image Enhancement

영상의 화질 개선을 위한 Multi-Scale Retinex 기반의 적응적 언샤프 마스킹 필터 설계

  • Kim, Ju Young (Dept. of Electronics and Computer Eng., Seokyeong University) ;
  • Kim, Jin Heon (Dept. of Computer Eng., Seokyeong University)
  • Received : 2018.01.12
  • Accepted : 2018.01.22
  • Published : 2018.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose an image enhancement method based on Multi-Scale Retinex theory that designs Unsharp Masking Filter (UMF) and emphasizes the contrast ratio adaptively. Unsharp Masking (UM) technique emphasizes image sharpness and improves contrast ratio by adding high frequency component to the original image. The high frequency component is obtained by differentiating between original image and low frequency image. In this paper, we present how to design an UMF kernel and to adaptively apply it to increase the contrast ratio according to multi-scale retinex theory which resembles human visual system. Experimental results show that the proposed method has better quantitative performance indexes such as PSNR, ambe & SSIM and better qualitative feature like halo artifact suppression.

Keywords

References

  1. E.H. Land and J.J. McCann, “Lightness and Retinex Theory,” Journal of the Optical Society of America, Vol. 61, No. 1, pp. 1-11, 1971. https://doi.org/10.1364/JOSA.61.000001
  2. D.J. Jobson, D. Jobson, and G. Woodell, “Properties and Performance of a Center/Surround Retinex,” IEEE Transactions on Image Processing, Vol. 6, No. 3, pp. 451-462, 1997. https://doi.org/10.1109/83.557356
  3. E.H. Land, “An Alternative Technique for the Computation of the Designator in the Retinex Theory of Color Vision,” National Academy of Sciences of the United States of America, Vol. 83, No. 10, pp. 3078-3080, 1986. https://doi.org/10.1073/pnas.83.10.3078
  4. A.C. Hurlbert and T.A. Poggio, “Synthesizing a Color Algorithm from Examples,” Science, Vol. 239, No. 4839, pp. 482-485, 1988. https://doi.org/10.1126/science.239.4839.482
  5. H.G. Kim, D.B. Lee, and B.C. Song, "Adaptive Unsharp Masking Using Bilateral Filter," Journal of the Institute of Electronics and Information Engineers, Vol. 19, No. 11, pp. 56-63 2012.
  6. L. Meyla, “High Dynamic Range Image Rendering With a Retinex-based Adaptive Filter,” IEEE Transactions on Image Processing, Vol. 15, No. 9, pp. 2820-2830, 2006. https://doi.org/10.1109/TIP.2006.877312
  7. Z. Rahman, G.A. Woodell, and D.J. Jobson, "Multi-scale Retinex for Color Image Enhancement," Proceeding of IEEE International Conference on Image Processing, pp. 1003-1006, 1996.
  8. Z. Rahman, G.A. Woodell, and D.J. Jobson, "A Comparison of the Multiscale Retinex with Other Image Enhancement Techniques," Proceeding of the Information Systems and Technology 50th Anniversary Conference, 1997.
  9. H.S. Cha and S.H. Hong, "Advanced Retinex Algorithm for Image Enhancement," Journal of Korea Multimedia Society, Vol. 16, No. 1, pp. 29-41, 2013. https://doi.org/10.9717/kmms.2013.16.1.029
  10. C.Y. Jamg, J.H. Lim, and Y.H. Kim, "A Fast Multi-scale Retinex Algorithm Using Dominant SSR in Weights Selection," Proceeding of International Symposium on Computers and Communications, pp 37-40, 2012.
  11. A. Mulyantini and H.K. Choi, "Color Image Enhancement Using a Retinex Algorithm with Bilateral Filtering for Images with Poor Illumination," Journal of Korea Multimedia Society, Vol. 19, No. 2, pp. 233-239, 2016. https://doi.org/10.9717/kmms.2016.19.2.233
  12. J.M Hwang, O. S. Kwon, "Image Enhancement using intensity Deviation of Boundary Regions", Journal of the Institue of Electronics and Information Engineers, Vol. 51, No. 12, pp. 140-149, 2014 https://doi.org/10.5573/ieie.2014.51.12.140
  13. C.Y. Jang, J. Hyun, S. Cho, H.S. Kim, and Y.H. Kim, "Adaptive Selection of Weights in Multi-scale Retinex Using Illumination and Object Edges," Proceeding of the International Conference on Image Processing, Computer Vision, and Pattern Recognition, The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing, p. 1, 2012.
  14. 'Image Emotion-download Image Data-set, http://www.imageemotion.org/ (accessed Oct., 5, 2017)
  15. Wikipedia, Peak Signal-to-Noise Ratio, https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio (accessed Oct., 10, 2017)
  16. S.D. Chen and A.R. Ramli, "Minimum Mean Brightness Error Bi-histogram Equalization in Contrast Enhancement," IEEE Transactions on Consumer Electronics, Vol. 49, Issue 4, pp. 1310-1319, 2003. https://doi.org/10.1109/TCE.2003.1261234
  17. Z. Wang, E.P. Simoncelli, and A.C. Bovik, "Multi-scale Structural Similarity for Image Quality Assessment," Proceeding of the 37th IEEE Asilomar Conference on Signals, Systems, and Computers, Vol. 2, pp. 9-12, 2013.
  18. T.H. Lee, W.J. Song, "Adaptive windowing for sharpness enhancement and halo reduction", The Institute of electronics of korea, pp. 893-894, 2008.