The task of image enhancement is focused on restoring and clarifying the corrupted images to improve their quality, and image enhancement methods has been widely applied to numerous image analysis techniques including pattern recognition, image fusion, image segmentation and so forth. Among the various methods used to enhance the image, algorithms created from retinex theory have received more and more attention and have been commonly used in many applications. This paper describes a retinex theory based method for contrast and illuminance enhancement in images of low light or unevenly illuminated scenes. This method firstly transformsimage from RGB color space to HSV colorspace, and decomposes the value channel using dual-tree complex wavelet transform. Then, to process the lesser frequency component of the image, an improved adaptive local tone mapping method is utilized and wavelet shrinkage method and fuzzy enhancement method are applied to denoise and enhance the reconstructed and a statistical histogram optimization method is used. After that, the enhanced value channel is the image is transformed back to RGB color space. Findings from experiments support the method suggested by this paper which performs very well with enhancement and de-noise of the corrupted images.

Retinex, dual tree complex wavelet transform, adaptive local tone mapping, wavelet shrinkage, fuzzy, histogram optimization

[1] Parth Bhatt, Sachin Patel”, Image Enhancement Using Various Interpolation Methods”, International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555, Vol. 2, No.4, August 2012.
[2] Mrs. Anjali Chandra1, Bibhudendra Acharya2, Mohammad Imroze Khan3 “RETINEX IMAGE PROCESSING: IMPROVING THE VISUAL REALISM OF COLOR IMAGES” International Journal of Information Technology and Knowledge Management, July-December 2011, Volume4, No. 2, pp. 371-377
[3] E. Land and J. McCann, “Lightness and retinex theory,” J. Opt. Soc. Amer., vol. 61, no. 1, pp. 1–11, Jan 1971.
[4] E. H. Land, “The retinex theory of color vision,” Scientific American, vol. 237, no. 6, pp. 108–128, Dec 1977.
[5] B. Li, S. Wang, and Y. Geng, “Image enhancement based on retinex and lightness decomposition,” in Proc. 18th IEEE Conf. on Image Processing, September 2011, pp. 3417–3420.
[6] J.H. Jang, S.D. Kim, and J.B. Ra, “Enhancement of optical remote sensing images by subband-decomposed multiscaleretinex with hybrid intensity transfer function,” IEEE Geosci. Remote Sens. Lett., vol. 8, no. 5, pp. 983–987, 2011
[7] R. C. Gonzalez, R. E. Woods, ʹDigital Image Processingʹ,2nd ed., Prentice Hall.
[8] E. Provenzi, C. Gatta, M. Fierro, and A. Rizzi, “A spatially variant whitepatch and gray-world method for color image enhancement driven by local contrast,” vol. 30, no. 10, pp. 1757–1770, 2008.
[9] Khare, A., Khare, M., Jeong, Y., Kim, H., and Jeon, M.: ‘Despecklingofmedical ultrasound images using daubechies complex wavelet transform’, Signal Process., 2010, pp. 428–439.
[10] Nick Kingsbury,”Shift invariant properties of the dual tree Complex Wavelet Transform” IEEE Trans. On signal processing letters, pp. 9-11,1999.
[11] H. Demirel, G. Anbarjafari, and S. Izadpanahi, “Improved motionbased localized super resolution technique using discrete wavelet transform for low resolution video enhancement,” in Proc. 17th Eur. Signal Process. Conf., Glasgow, Scotland, pp1097–1101, Aug. 2009,
[12] H. Demirel and G. Anbarjafari, “Satellite image resolution enhancement using complex wavelet transform,” IEEE Geoscience and Remote Sensing Letter, vol. 7, no. 1, pp. 123–126, Jan. 2010
[13] Felix C. A. Fernandes, Rutger L. C.,”A New Framework for Complex Wavelet Transform” IEEE Trans. On signal processings, vol.51,no.7,July,2003.
[14]X. Yu, X. Luo, G. Lyu and S. Luo, "A novel Retinex based enhancement algorithm considering noise," 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), Wuhan, China, 2017, pp. 649-654.