Improving Image Quality Based on Feature Extraction and Gaussian Model

Subject Areas : Majlesi Journal of Telecommunication Devices

Alireza  Alirezaei Shahraki ¹ (Islamic Azad University, Mobarakeh Branch/Faculty of Electrical engineering, Mobarakeh,Isfahan, Iran)
Mehran  Emadi ² ()

Received: 2019-03-09 Accepted : 2020-02-26 Published : 2019-03-01

Keywords: Improvement of image quality, Histogram, Gaussian combination model, Qualitative and quantitative evaluation, Feature extraction,

Abstract :

By expanding the use of digital images in various areas of everyday life, such as medicine, identification, satellite imagery, and even personal cameras and machine vision, it is felt more effective in applying quality improvements to the images used. The low-quality images in the machine's vision can expose the efficacy of later processing, such as feature extraction, classification, and pattern recognition. In this thesis, a new method for improving the quality of images based on the extraction of Godin’s combined feature and model has been proposed. Based on the fact that each homogeneous region in the image has a Gaussian distribution histogram, this distribution can be divided into smaller histograms. For the histogram division efficacy, the image is transmitted from the RGB space to the HSV space and the histogram division is applied to the severity region, and the histogram is applied to each sub Histogram based on the statistical characteristics, and the image. Improved results are returned to the RGB color space. Several qualitative and quantitative criteria have been used to evaluate the proposed method. Qualitative comparison results show improved image quality compared to histogram equivalence methods and linear contrast traction. Quantitative evaluation criteria, such as entropy and spatial frequency, as well as signal to noise ratio, and peak signal to noise ratio, are generally proposed for superiority of the method.

References:

[1] Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc, Upper Saddle River (2006)

[2] Jobson, D., Rahman, Z., Woodell, G.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (2017)

[3] Mukherjee, J., Mitra, S.: Enhancement of color images by scaling the dct coefficients. IEEETrans. Image Process. 17(10), 1783–1794 (2018)

[4] Agaian, S., Silver,B., Panetta, K.:Transform coefficient histogrambased image enhancement algorithms using contrast entropy. IEEE Trans. Image Process. 16(3), 741–758 (2007)

[5] Hasikin, K.,Mat Isa, N.A.:Adaptive fuzzy contrast factor enhancement technique for low contrast and nonuniform illumination images. Signal Image Video Proces. 8(8), 1591–1603 (2014)

[6] Jafar, I., Ying, H.: Multilevel component-based histogram equalization for enhancing the quality of grayscale images. In: IEEE EIT, pp. 563–568 (2013)

[7] Abdullah-Al-Wadud, M., Kabir, M., Dewan, M., Chae, O.: A dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consum. Electron. 53(2), 593–600 (2017)

[8]Y. Xia, C. Sun, and W. X. Zheng, Discrete-time neural network for fast solving large linear L1 estimation problems and its application to image restoration, IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 5,pp. 812–820, May 2012

[9] Chen, S., Rahman, A.: Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans. Consum. Electron. 49(4), 1301–1309 (2013)

[10] Kim, T.K., Paik, J.K., Kang, B.S.: Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Trans. Consum. Electron. 44(1), 82–87 (2008)

[11] Weilong Hou, Blind. Image Quality Assessment via Deep Learning, Vol.26, pp. 1275 – 1286, 2015.

[12] Tsai, C.M., Yeh, Z.M.: Contrast enhancement by automatic and parameter-free piecewise linear transformation for color images. IEEE Trans. Consum. Electron. 54(2), 213–219 (2008)

[13] Cherifi,D., Beghdadi,A., Belbachir,A.H.: Color contrast enhancement method using steerable pyramid transform. Signal Image Video Proces. 4(2), 247–262 (2010)

[14] Xu, Z., Wu, H.R., Yu, X., Qiu, B.: Colour image enhancement by virtual histogram approach. IEEE Trans. Consum. Electron. 56(2), 704–712 (2010)

[15] Figueiredo, M., Jain, A.: Unsupervised learning of finite mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 381–396 (2012)

[16] Mehdi Kamandar, Automatic color image contrast enhancement using Gaussian mixture modeling, piecewise linear transformation, and monotone piecewise cubic interpolant, May 2018, Volume 12, Issue 4, pp 625–632

[17] Lin Zhang , A Feature-Enriched Completely Blind Image Quality Evaluator, IEEE TRANSACTIONS ON IMAGE PROCESSING, 2015.

[18] K. Zhang, X. Gao, D. Tao, and X. Li, Single image super-resolution with multiscale similarity learning, IEEE Trans. Neural Netw. Learn.Syst., vol. 24, no. 10, pp. 1648–1659, Oct. 2013.

[19] Celik, T., Tjahjadi, T.: Automatic image equalization and contrast enhancement using Gaussian mixture modeling. IEEE Trans. Image Process. 21(1), 145–156 (2012)

[20] Kim, Y.T.: Contrast enhancement using brightness preserving bihistogram equalization. IEEE Trans. Consum. Electron. 43(1), 1–8 (2007).

[21] J. Yu, X. Gao, D. Tao, X. Li, and K. Zhang, A unified learning framework for single image super-resolution, IEEE Trans. Neural Netw.Learn. Syst., vol. 25, no. 4, pp. 780–792, Apr. 2014.

[22] W. Xue, L. Zhang, and X. Mou, Learning without human scores for blind image quality assessment, in Proc. IEEE Conf. Comp. Vis. Pattern Recog, pp. 995-1002, 2013.