Low-Light Image Enhancement Using Sped-Up solver Method via Illumination Map Estimation

Authors

  • V. Chinnapudevi  Department of ECE, Brindavan Institute of technology and science, Kurnool, Andhra Pradesh, India
  • K. Aparna  Department of ECE, Brindavan Institute of technology and science, Kurnool, Andhra Pradesh, India
  • D. Sunitha  Department of ECE, Brindavan Institute of technology and science, Kurnool, Andhra Pradesh, India
  • C. Swetha  Department of ECE, Brindavan Institute of technology and science, Kurnool, Andhra Pradesh, India

Keywords:

Illumination Estimation, Illumination Transmission,Low Light Image Enhancement.

Abstract

In the present scenario digital images are playing very important role in several applications. When one captures images during night times or low light conditions, the images often suffer from low visibility. In order to improve the quality of an image, image enhancement can be used. This type of low light images may decrease the performance of computer vision and other multimedia algorithms that are essentially designed for high-quality inputs. In order to estimate the high quality image in this paper we proposed a low light image enhancement method. In this method, first we estimate the illumination of each pixel individually by finding the maximum value in R, G and B channels. Further we refine the initial illumination map by imposing a structure prior on it, as the final illumination map. The noise can be removed by using BM3D method. For a low light image enhancement we consider the one parameter called Lightness order error (LOE) which gives the light source direction and the lightness variations.

References

  1. E. Pisano et al., "Contrast limited adaptive histogram equalization image processing to improve the detection of simulated speculations in dense mammograms," J. Digit. Image., vol. 11, no. 4, pp. 193–200, 1998.
  2. C. Lee, C. Lee, and C.-S. Kim, "Contrast enhancement based on layered difference representation of 2D histograms," IEEE Trans. Image Process., vol. 22, no. 12, pp. 5372–5384, Dec. 2013.
  3. D. J. Jobson, Z.-U. Rahman, and G. A. Woodell, "Properties and performance of a centre/surround Retinex," IEEE Trans. Image Process.,vol. 6, no. 3, pp. 451–462, Mar. 1997.
  4. L. Li, R. Wang, W. Wang, and W. Gao, "A low-light image enhancement method for both denoising and contrast enlarging," in Proc. ICIP, 2015,pp. 3730–3734
  5. R. Grosse, M. Johnson, E. Adelson, and W. Freeman, "Ground-truthdataset and baseline evaluations for intrinsic image algorithms," in Proc.ICCV, 2009, pp. 2335–2342.
  6. K. Zhang, L. Zhang, and M. Yang, "Real-time compressive tracking," in Proc. ECCV, 2014, pp. 866–879.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-04-30

Issue

Volume 4, Issue 4, March-April-2018

Section

Research Articles

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Copyright (c) IJSRSET

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
V. Chinnapudevi, K. Aparna, D. Sunitha, C. Swetha, " Low-Light Image Enhancement Using Sped-Up solver Method via Illumination Map Estimation , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 4, pp.1284-1288, March-April-2018.