Block-based Contrast Enhancement Algorithm for X-ray Images

X-ray 영상을 위한 블록 기반 대비 개선 기법

  • Received : 2015.06.17
  • Accepted : 2015.09.24
  • Published : 2015.10.25


If typical contrast enhancement algorithms for natural images are applied to X-ray images, they may cause artifacts such as overshooting or produce unnatural visual quality because they do not consider inherent characteristics of X-ray images. In order to overcome such problems, we propose a locally adaptive block-based contrast enhancement algorithm for X-ray images. After we derive a weighted cumulative distribution function for each block, we apply it to each block for contrast enhancement. Then, we obtain images that are removed from block effect by adopting block-based overlapping. In post-processing, we obtain the final image by emphasizing high frequency components. Experimental results show that the proposed block-based contrast enhancement algorithm provides at maximum 5-times higher visual quality than the exiting algorithm in terms of quantitative contrast metric.


  1. R. C. Gonzalez, R. E. Woods, Digital image processing. 2nd ed. Reading, MA. Addison-Wesley, pp. 85-103, 1992.
  2. M. Abdullah-Al-Wadud, M. Hasanul Kabir, M. Ali Akber Dewan, and Oksam Chae, "A dynamic histogram equalization for image contrast enhancement," IEEE Trans. Consumer Electron., vol. 53, no. 2, pp. 593-600, May 2007.
  3. C. H. Lu, H. Y. Hsu, L. Wang, "A new contrast enhancement technique by adaptively increasing the value of histogram," in IEEE International Workshop on Imaging Systems and Techniques, China, pp. 407-411, 2009.
  4. S. D. Chen and A. Rahman Ramni, "Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation," IEEE Trans. Consumer Electron., vol. 49, no. 4, pp. 1301-1309, Nov. 2003.
  5. G. H. Park, H. H. Cho, M. R. Choi, "A contrast enhancement method using dynamic range separate histogram equalization," IEEE Trans. Consumer Electron., vol. 54, no. 4, pp. 1981-1987, Nov. 2008.
  6. S. C. Huang, F. C. Cheng, and Y, S. Chiu, "Efficient contrast enhancement using adaptive gamma correction with weighting distribution," IEEE Trans. Image Processing, vol. 22, no. 3, pp. 1032-1041, Mar. 2013.
  7. K. Kokufuta and T. Maruyama, "Real-time processing of local contrast enhancement on FPGA," in International Conference on Field Programmable Logic and Applications, Prague, pp. 288-293, 2009.
  8. J. A. Stark, "Adaptive image contrast enhancement using generalizations of histogram equalization," IEEE Trans. Image Processing, vol. 9, no. 5, pp. 889-896, May 2000.
  9. T. K. Kim, J. K. Paik and B. S. Kang, "Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering," IEEE Trans. Consumer Electron., vol. 44, no. 1, pp. 82-86, Feb. 1998.
  10. K. Zuiderveld, "Contrast limited adaptive histogram equalization," Graphics Gems IV, pp. 474-485. Academic Press Professional, Inc., 1994.
  11. B. Liu, W. Jin, Y. Chen, C. Liu, and L. Li, "Contrast enhancement using non-overlapped sub-blocks and local histogram projection," IEEE Trans. Consumer Electron., vol. 57, no. 2, pp. 583-588, May 2011.
  12. T. Qiu, A. Wang, N. Yu, and A. Song, "LLSURE: local linear SURE-based edge-preserving image filtering," IEEE Trans. Image Processing, vol. 22, no. 1, pp. 80-90, Jan. 2013.
  13. J. H. Jang, B. Choi, S. D. Kim, and J. B. Ra, "Sub-band decomposed multiscale retinex with space varying gain," in Proc. IEEE Int. Conf. Image Process.(ICIP), pp. 3168-3171, 2008.
  14. S. S. Agaian, K. Panetta, and A. Grigoryan, "Transform based image enhancement with performance measure," IEEE Trans. Image Processing, vol. 10, no. 3, pp. 367-382, Mar. 2001.
  15. S. S. Agaian, B. Silver, and K. A. Panetta, "Transform coefficient histogram-based image enhancement algorithms using contrast entropy," IEEE Trans. Image Processing, vol. 16, no. 3, pp. 471-758, Mar. 2007.
  16. A. Saleem, A. Beghdadi, and B. Boashash, "Image fusion-based contrast enhancement" EURASIP Journal on Image and Video Processing, 2012.