Automatic image colorization based on SVD and lab color space

  • Abstract
  • Keywords
  • References
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  • Abstract

    Images colorization is the operation of append colors to the grayscale image by using a reference color image. The main problem of  coloring a grayscale image involves constructing three dimensional image from one dimensional array. The current paper developed a fully automatic image colorization system based on the singular value decomposition (SVD) transformation. SVD used to measuring the best pixel in the reference image proper to colorize the gray image, depending on comparing the pixel value and their neighbor values of the gray image with pixel value and their neighbors in the reference image. Using the color space is more suitable to many digital image processing than the RGB, for that the author suggested to convert reference image to the CIELab color space in this proposal. Gray   image normalized according to the (L channel) of reference image. Ultimately, the intensity of the selected pixel in the gray image   combine with (a, b channels) values of the best pixel from CIELab color space of reference image, which is finally converted to RGB color image. The suggested algorithm is the first algorithm utilized the SVD for image colorization which give good and promised results compared with other methods, and can be enhanced in the future.



  • Keywords

    Colorization; Gray Image; Image Processing; Lab Color Space; SVD.

  • References

      [1] Guillaume Charpiat, Matthias Hofmann, Bernhard Schölkopf, Automatic Image Colorization Via Multimodal Predictions, European Conference on Computer Vision (ECCV) (2008), pp 126-139.

      [2] Upasana Bisht, Tushar Patnaik, “Overview of Automatic Image Colorization Schemes”, International Journal of Advanced Engineering and Global Technology, Vol-03, Issue-10, (2015) pp 1283-1287.

      [3] Smriti Kumar, and Ayush Swarnkar. Gray image colorization in YCbCr color space, Emerging Technology Trends in Electronics, Communication and Networking (ET2ECN), 2012 1st International Conference on IEEE, (2012). ‏

      [4] Wang, Huanjuan, et al., Novel colorization method based on correlation neighbourhood similarity pixels’ priori, Signal Processing (ICSP), 2012 IEEE 11th International Conference on. Vol. 2. IEEE, (2012).

      [5] Devi, Mandalapu Sarada, and Ankita Mandowara, Extended performance comparison of pixel window size for colorization of grayscale images using YUV color space, Engineering (NUiCONE), 2012 Nirma University International Conference on IEEE, (2012). ‏

      [6] S. D., Garg, R. H., Ghewade, S. A., Jagdale, P. A., & Mahajan, Performance assessment of assorted similarity measures in gray image colorization using LBG vector quantization algorithm, International Conference on Industrial Instrumentation and Control (ICIC), IEEE, (2015).

      [7] Hu, Min, Bo Ou, and Yi Xiao, “Efficient image colorization based on seed pixel selection”, Multimedia Tools and Applications, vol 76, issue 22, pp 23567–23588 (2017).

      [8] Deshpande, Aditya, Jason Rock, and David Forsyth, Learning large-scale automatic image colorization, Proceedings of the IEEE International Conference on Computer Vision, (2015).‏

      [9] Okura, Fumio, et al., Unifying color and texture transfer for predictive appearance manipulation, Computer Graphics Forum. Vol. 34. No. 4. 2015.‏

      [10] Li, Bo, Yu-Kun Lai, and Paul L. Rosin, “Example-based image colorization via automatic feature selection and fusion”, Neurocomputing 266 (2017): 687-698.‏

      [11] Margulis, Dan, Photoshop Lab Color: The Canyon Conundrum and Other Adventures in the Most Powerful Color space.

      [12] Kumar, Smriti, and Ayush Swarnkar, Colorization of gray scale images in lαβ color space using mean and standard deviation, Electrical, Electronics and Computer Science (SCEECS), 2012 IEEE Students' Conference on. IEEE, 2012.

      [13] Asmare, Melkamu Hunegnaw, Vijanth Sagayan Asirvadam, and Lila Iznita, Color space selection for color image enhancement applications, 2009 International Conference on Signal Acquisition and Processing. IEEE, 2009.‏

      [14] Nidhal Khdhair El Abbadi, Data Hiding Schemes Based on Singular Value Decomposition: Handbook of Research on Threat Detection and Countermeasures in Network Security, IGI Global, (2015), DOI: 10.4018/978-1-4666-6583-5.ch012.




Article ID: 26186
DOI: 10.14419/ijet.v7i4.26186

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