Improving Image Dynamic Range For An Adaptive Quality Enhancement Using Gamma Correction
الموضوعات : Image Processing
1 - Shahrood University of Technology
الکلمات المفتاحية: Image Enhancement , Gamma Correction , Segmentation , Co-Occurrence Matrix , Homogeneity,
ملخص المقالة :
This paper proposes a new automatic image enhancement method by improving the image dynamic range. The improvement is performed via modifying the Gamma value of pixels in the image. Gamma distortion in an image is due to the technical limitations in the imaging device, and impose a nonlinear effect. The severity of distortion in an image varies depends on the texture and depth of the objects. The proposed method locally estimates the Gamma values in an image. In this method, the image is initially segmented using a pixon-based approach. Pixels in each segment have similar characteristics in terms of the need for Gamma correction. Then the Gamma value for each segment is estimated by minimizing the homogeneity of co-occurrence matrix. This feature can represent image details. The minimum value of this feature in a segment shows maximum details of the segment. The quality of an image is improved once more details are presented in the image via Gamma correction. In this study, it is shown that the proposed method performs well in improving the quality of images. Subjective and objective image quality assessments performed in this study attest the superiority of the proposed method compared to the existing methods in image quality enhancement.
[1] M.Y. Nam, P.K. Rhee, “An efficient Face Recognition for Variant Illumination Condition”, The International Symposium on Intelligent Signal Processing and Communication Systems, 2004, pp. 111-115.#
[2] Y. Shi, J. Yang, R.Wu, “Reducing Illumination Based on Nonlinear Gamma Correction”, The IEEE International Conference on Image Processing, San Antonio, 2007, pp. 529-532.#
[3] R.C. Gonzalez, R. E. Woods, “Digital Image Processing Prentice Hall, Upper Saddle River”, NJ 07458, 2002.#
[4] M. Tiawari, S. S. Lamba, Bh. Gupta, “An approach for visibility improvement of dark color images using adaptive gamma correction and DCT-SVD”, International Workshop on Pattern Recognition, 2016.#
[5] H. Farid, “Blind Inverse Gamma Correction. IEEE Transactions on Image Processing”, Vol. 10, No. 10, 2001, pp.1428-1433.#
[6] S. Lee, “Content-based Image Enhancement in the Compressed Domain based on Multi-scale α-rooting Algorithm”, Pattern Recognition Letters, 2006, Vol. 27, No. 10, pp. 1054-1066.#
[7] Q. Chen, X. Xu, Q. Sun, D. Xia, “A Solution to the Deficiencies of Image Enhancement”, Signal Processing, 2010, Vol. 90, No. 1, pp. 44-56.#
[8] S. Asadi Amiri, H. Hassanpour, A.K. Pouyan, “Texture Based Image Enhancement Using Gamma Correction”, Middle-East Journal of Scientific Research, 2010, Vol. 6, pp. 569-574.#
[9] M. Farshbaf Doustar, H. Hassanpour, “A Locally Adaptive Approach for Image Gamma Correction”, Signal Processing and their Applications, 2010, pp. 73-76.#
[10] H. Hassanpour, S. Asadi Amiri, “Image Enhancement Using Gamma Information”, Journal of Signal and Data Processing, 2012, Vol. 1, No. 15, pp. 25-32.#
[11] H. Hassanpour, S. Asadi Amiri, “Image Quality Enhancement Using Pixel Wise Gamma Correction via SVM Classifier”, International Journal of Engineering, 2011, Vol. 24, No. 4, pp. 301-311.#
[12] H. Hassanpour, S. Asadi Amiri, “A Preprocessing Approach for Image Analysis Using Gamma Correction”, International Journal of Computer Applications, 2012, Vol. 38, No. 12, pp. 38-46.#
[13] S. Asadi Amiri, E. Moudi, “Image Quality Enhancement in Digital Panoramic Radiograph”, Journal of AI and Data Mining, 2014, Vol. 2, No. 1, pp. 1-6.#
[14] H. Hassanpour, H. Yousefian, “An Improved Pixon-Based Approach for Image Segmentation”, International Journal of Engineering, 2011, Vol. 24, No. 1, pp. 25-35.#
[15] F. Yang, T. Jiang, “Pixon-based Image Segmentation with Markov Random Fields”, IEEE Transactions on Image Processing, 2003, Vol. 12, No. 12, pp. 1552-1559.#
[16] R. Jobanputra, D. Clausi, “Preserving Boundaries for Image Texture Segmentation using Grey Level Co-Occurring probabilities”, Pattern Recognition, 2006, Vol. 39, No. 2, pp. 234-245.#
[17] R.M. Haralick, K. Shanmugan, I. Dinstein, “Textural features for image classification”, IEEE Transactions on Systems, Man, and Cybernetics, 1973, Vol. 3, No. 6, pp. 610-621.#
[18] Z. Wang, A.C. Bovik, “Mean Squared Error: Love It or Leave It? A New Look at Signal Fidelity Measures”, IEEE Signal Processing, 2009, Vol. 26, No. 1, pp. 98-117.#
[19] Zh. Wang, L. Li, Sh. Wu, Y. Xia, Zh. Wan, C. Cai, “A New Image Quality Assessment Algorithm based on SSIM and Multiple Regressions”, International Journal of Signal Processing, Image Processing and Pattern Recognition, 2015, Vol. 8, No. 11, pp. 221-230.#
