Development of the SR Based Auto-tuning Image Enhancement System and Evaluation of the Enhanced Image Quality using PQM

Noriko Kojima, Bikash Lamsal, Naofumi Matsumoto, Mitsuo Yamashiro


This paper proposes the development and evaluation of an auto-tuning stochastic resonance (SR) for image enhancement on images under various illumination conditions. Perceptual Quality Metric (PQM) is used for evaluating and quantifying the image quality. The current process is developed being based on our previous works related to the image enhancement by using the manual tuning stochastic resonance. The process was performed by adding the random noise and threshold in an image. The process works properly in the dark and very low contrast images as well as bright images. This image enhancement system works on dark and bright images as well. The system was tested with the face detection algorithm on the dark and illumination variant images. In this paper, we present the idea of auto-tuning of the SR iteration with random noise and threshold value 0 by using the process related to the histogram calculation, mean and median. In this paper, we performed various experiments on object and human detection as well under different conditions and confirmed the effectiveness of our auto-tuning SR based image enhancement algorithm. Finally, we conducted experiments using Perceptual Quality Metric which is an image quality metric to apply the proposed algorithm in various fields.

Full Text:



B. Lamsal, N. Kojima and N. Matsumoto, “Impact of the stochastic resonance on dark and illumination variant images for face detection”, Journal of the Institute of Industrial Applications Engineers, vol.3, no.4, pp.167-173, 2015.

N. Kojima, B. Lamsal and N. Matsumoto, “An adaptive tuning stochastic resonance approach for image enhancement on illumination variant images”, Journal of the Institute of Industrial Applications Engineers, vol.5, no.1, pp.1-8, 2017.

N. Kojima, N. Matsumoto and M. Yamashiro, “Image enhancement under severe lighting conditions using stochastic resonance”, LIFE2017, 2017. (in Japanese)

E. Simonotto, M. Riani, S. Charles, M. Roberts, J. Twitty and F. Moss, “Visual perception of stochastic resonance”, Phys. Rev. Lett., 78(6), pp.1186-1189, 1997.

R. Chouhan, R. K. Iha and P. K. Biswas, “Wavelet-based contrast enhancement of dark images using dynamic stochas- tic resonance”, ICVGIP ’12, Mumbai, India, 2012.

R.K.JhaandR.Chouhan, “Noise-induced c-ontrast enhance- ment using stochastic resonance on singular values”, SIViP 8, pp.339-347, 2014.

R. Benzi, A. Sutera and A. Vulpiani, “The mechanism of Stochastic Resonance”, J. Physics. A: Math and General, vol.14, pp. L453 -L457,

J. J. Collins, C. C. Chow and T. T. Imhoff, “Stochastic resonance without tuning”, Letters to NATURE, vol.376, pp.236- 238, 1995.

S. Kasai, K. Miura and Y. Shiratori, “Threshold-variation enhanced adaptability of response in a nanowire field-effect transistor network”, Applied Physics Letters, Vol.96, No.19, pp.194102, 2010.

B. Lamsal and N. Matsumoto, “Effects of the Unscented Kalman filter process for high-performance face detector”, Intl. J. of Information and Electronics Engineering, Vol.5, No.6, pp.454-459, 2015.

Z. Wang, H. R. Sheikh and A. C. Bovik, “No-reference per- ceptual quality assessment of JPEG compressed images”, Proc. IEEE Int. Conf. Image Processing, vol.1, pp.477-480, 2002.



  • There are currently no refbacks.

Online edition: ISSN 2187-8811 Print edition: ISSN 2188-1758