Purple Fringing Aberration Detection Based on Content-Adaptable Thresholds

  • Kannan KarthikEmail author
  • Parveen Malik
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 79)


Purple fringe aberration (PFA) patterns stem from specific defects in certain camera sensor grids, leading to the fraying of edges near high-contrast regions. Much of the literature deploys predefined, absolute, experimentally determined static thresholds for detecting purple fringes. Given the potential diversity in the spectral signature of the local light source, these fringes may not have a static distribution. It is therefore important to make the detection procedure content-adaptable and in tune with the environmental settings. In this paper, we propose a PFA detection procedure in the Y-Cb-Cr CHROMATIC space, by first using a global relativistic Y-channel-gradient threshold for detecting the high-contrast regions and then use the fact that PURPLE and GREEN are antipodes of each other to segregate PURPLE fringes reliably. Comparisons with the state-of-the-art detection approaches are presented. The advantage with the proposed approach rests with the fact that the threshold is content-adaptable and non-static and can therefore be used to pick up diverse fringe patterns (not necessarily confined to the seat of PURPLE).


Purple fringing aberration Content-adaptable threshold Purple and green antipodes 


  1. 1.
    Nakamura, J.: Image sensors and signal processing for digital still cameras. CRC Press (2005).Google Scholar
  2. 2.
  3. 3.
    Yerushalmy, I., Hel-Or, H.: Digital image forgery detection based on lens and sensor aberration. Volume 92., Springer (2011) 71–91.Google Scholar
  4. 4.
    Kang, S.: Automatic removal of purple fringing from images (July 5 2007) US Patent App. 11/322,736.Google Scholar
  5. 5.
    Kim, B., Park, R.: Automatic detection and correction of purple fringing using the gradient information and desaturation. In Proceedings of the 16th European Signal Processing Conference. Volume 4. (2008).Google Scholar
  6. 6.
    Ju, H.J., Park, R.H.: Colour fringe detection and correction in ycbcr colour space. Image Processing, IET 7(4) (June 2013) 300–309.Google Scholar
  7. 7.
    Chung, S.W., Kim, B.K., Song, W.J.: Detecting and eliminating chromatic aberration in digital images. In: Image Processing (ICIP), 2009 16th IEEE International Conference on. (Nov 2009) 3905–3908.Google Scholar
  8. 8.
    Kim, B.K., Park, R.H.: Detection and correction of purple fringing using colour desaturation in the xy chromaticity diagram and the gradient information. Image and Vision Computing 28(6) (2010) 952–964.Google Scholar
  9. 9.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Electronics and Electrical EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia

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