Advertisement

A Contrast Enhancement Framework with JPEG Artifacts Suppression

  • Yu Li
  • Fangfang Guo
  • Robby T. Tan
  • Michael S. Brown
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)

Abstract

Contrast enhancement is used for many algorithms in computer vision. It is applied either explicitly, such as histogram equalization and tone-curve manipulation, or implicitly via methods that deal with degradation from physical phenomena such as haze, fog or underwater imaging. While contrast enhancement boosts the image appearance, it can unintentionally boost unsightly image artifacts, especially artifacts from JPEG compression. Most JPEG implementations optimize the compression in a scene-dependent manner such that low-contrast images exhibit few perceivable artifacts even for relatively high-compression factors. After contrast enhancement, however, these artifacts become significantly visible. Although there are numerous approaches targeting JPEG artifact reduction, these are generic in nature and are applied either as pre- or post-processing steps. When applied as pre-processing, existing methods tend to over smooth the image. When applied as post-processing, these are often ineffective at removing the boosted artifacts. To resolve this problem, we propose a framework that suppresses compression artifacts as an integral part of the contrast enhancement procedure. We show that this approach can produce compelling results superior to those obtained by existing JPEG artifacts removal methods for several types of contrast enhancement problems.

Keywords

Contrast Enhancement Dehazing JPEG Artifacts Removal Deblocking 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ancuti, C., Ancuti, C.O., Haber, T., Bekaert, P.: Enhancing underwater images and videos by fusion. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)Google Scholar
  2. 2.
    Aujol, J.F., Gilboa, G., Chan, T., Osher, S.: Structure-texture image decomposition–modeling, algorithms, and parameter selection. International Journal of Computer Vision 67(1), 111–136 (2006)CrossRefzbMATHGoogle Scholar
  3. 3.
    Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: Can plain neural networks compete with bm3d? In: IEEE Conference on Computer Vision and Pattern Recognition (2012)Google Scholar
  4. 4.
    Burt, P.J., Adelson, E.H.: The laplacian pyramid as a compact image code. IEEE Transactions on Communications 31(4), 532–540 (1983)CrossRefGoogle Scholar
  5. 5.
    Chiang, J.Y., Chen, Y.C.: Underwater image enhancement by wavelength compensation and dehazing. IEEE Transactions on Image Processing 21(4), 1756–1769 (2012)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Dong, W., Zhang, L., Shi, G.: Centralized sparse representation for image restoration. In: IEEE International Conference on Computer Vision (2011)Google Scholar
  7. 7.
    Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. ACM Transactions on Graphics (TOG) 21(3), 257–266 (2002)CrossRefGoogle Scholar
  8. 8.
    Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Transactions on Graphics (TOG) 27(3), 67 (2008)CrossRefGoogle Scholar
  9. 9.
    Fattal, R.: Single image dehazing. ACM Transactions on Graphics 27(3), 72 (2008)CrossRefGoogle Scholar
  10. 10.
    Foi, A., Katkovnik, V., Egiazarian, K.: Pointwise shape-adaptive dct for high-quality denoising and deblocking of grayscale and color images. IEEE Transactions on Image Processing 16(5), 1395–1411 (2007)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Goto, T., Kato, Y., Hirano, S., Sakurai, M., Nguyen, T.Q.: Compression artifact reduction based on total variation regularization method for mpeg-2. IEEE Transactions on Consumer Electronics 57(1), 253–259 (2011)CrossRefGoogle Scholar
  12. 12.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(12), 2341–2353 (2011)CrossRefGoogle Scholar
  13. 13.
    Jacobs, N., Burgin, W., Fridrich, N., Abrams, A., Miskell, K., Braswell, B.H., Richardson, A.D., Pless, R.: The global network of outdoor webcams: Properties and applications. In: ACM International Conference on Advances in Geographic Information Systems (2009)Google Scholar
  14. 14.
    Lee, K., Kim, D.S., Kim, T.: Regression-based prediction for blocking artifact reduction in jpeg-compressed images. IEEE Transactions on Image Processing 14(1), 36–48 (2005)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Lee, Y., Kim, H., Park, H.: Blocking effect reduction of jpeg images by signal adaptive filtering. IEEE Transactions on Image Processing 7(2), 229–234 (1998)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), 228–242 (2008)CrossRefGoogle Scholar
  17. 17.
    Majumder, A., Irani, S.: Perception-based contrast enhancement of images. ACM Transactions on Applied Perception 4(3), 17 (2007)CrossRefGoogle Scholar
  18. 18.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60(1), 259–268 (1992)CrossRefzbMATHGoogle Scholar
  19. 19.
    Sun, D., Cham, W.K.: Postprocessing of low bit-rate block dct coded images based on a fields of experts prior. IEEE Transactions on Image Processing 16(11), 2743–2751 (2007)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Tan, R.T.: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  21. 21.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: IEEE International Conference on Computer Vision, pp. 839–846 (1998)Google Scholar
  22. 22.
    Wang, C.Y., Lee, S.M., Chang, L.W.: Designing jpeg quantization tables based on human visual system. Image Communication 16(5), 501–506 (2001)Google Scholar
  23. 23.
    Wang, Y., Yang, J., Yin, W., Zhang, Y.: A new alternating minimization algorithm for total variation image reconstruction. SIAM Journal on Imaging Sciences 1(3), 248–272 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  24. 24.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  25. 25.
    Watson, A.: Dct quantization matrices visually optimized for individual images. In: Proceedings of the International Society for Optics and Photonics, vol. 1913, pp. 202–216 (1993)Google Scholar
  26. 26.
    Wedel, A., Pock, T., Zach, C., Bischof, H., Cremers, D.: An improved algorithm for tv-l 1 optical flow. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds.) Statistical and Geometrical Approaches to Visual Motion Analysis. LNCS, vol. 5604, pp. 23–45. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  27. 27.
    Yang, Y., Galatsanos, N.P., Katsaggelos, A.K.: Projection-based spatially adaptive reconstruction of block-transform compressed images. IEEE Transactions on Image Processing 4(7), 896–908 (1995)CrossRefGoogle Scholar
  28. 28.
    Yim, C., Bovik, A.: Quality assessment of deblocked images. IEEE Transactions on Image Processing 20(1), 88–98 (2011)CrossRefMathSciNetGoogle Scholar
  29. 29.
    Zakhor, A.: Iterative procedures for reduction of blocking effects in transform image coding. IEEE Transactions on Circuits and Systems for Video Technology 2(1), 91–95 (1992)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yu Li
    • 1
  • Fangfang Guo
    • 1
  • Robby T. Tan
    • 2
  • Michael S. Brown
    • 1
  1. 1.National University of SingaporeSingapore
  2. 2.SIM UniversitySingapore

Personalised recommendations