Image enhancement techniques are prominently used to analyze the image by enhancing key factors like contrast, resolution, and quality of the image. With the proper analysis of images, it is desirable to pre-process the image for resolution and contrast enhancement. We present here a new approach based on discrete wavelet transform (DWT), singular value decomposition (SVD) for image contrast and resolution enhancement, The contrast of the image is enhanced by maximum value fusion technique applied to the images created by using modified cuckoo search algorithm (CSA) and singular value decomposition separately. The masking approach is employed, for obtaining residual pixel value between original and scaled images independently. The resolution of the image is enhanced by combining interpolated high-frequency sub-band and maximum value fusion images. The proposed algorithm helps to minimize the noise artifacts and over enhancement problems. Experimental results are tested in terms of peak signal to noise ratio (PSNR) and absolute mean brightness error (AMBE). The proposed method shows better performance compared to other contrast and resolution enhancement techniques.
- Gonzalez, R.C.; Woods, R. Digital Image Processing, 3rd ed.; Pearson, INDIA, 2014, pp. 144–166.
- Demirel, H.; Anbarjafari, G.; Jahromi,M.N.S.Image Equalization based on singular value decomposition. IEEE international symposium on computer and information sciences Oct 2008, 27-29, pp.1-5.
- Demirel, H.; Anbarjafari, G. Satellite image resolution enhancement using complex wavelet transform. IEEE Geoscience and Remote Sensing Letter Jan 2010, 7, 123–126.
- Bhandari A K, Gadde M, Kumar A, Singh, GK. Comparative analysis of different wavelet filters for low contrast and brightness enhancement of multispectral remote sensing images. In: Proceedings of the IEEE international conference on machine vision and image processing (MVIP), p.81–6; 2012.
- Bhandari, A.K.; Kumar, A.; Padhy, P.K. Enhancement of low contrast satellite images using discrete cosine transform and singular value decomposition. World AcadSciEng Technology 2011; 79:35–41.
- Daniel, E.; Anitha, J. Optimum wavelet-based masking for contrast enhancement of medical images using enhanced cuckoo search algorithm.Elsevier, computers in biology and medicine 2016, 71, 419-155.
- Polesel, A.; Ramponi,g.; Mathews, V.J. Image enhancement via adaptive unsharp masking. ”, IEEE Transactions on Image Processing Mar 2000, 9, issue 3.
- Sandeepa K S, Basavaraj N Jagadale, J S Bhat. Wavelet-based medical image contrast and resolution enhancement by incorporating cuckoo search algorithm and SVD. 5th IIAE International Conference on Intelligent Systems and Image Processing 2017, DOI: 10.12792/icisip2017.034.
- C.Zuo, C.; Chen, O.; sui,X.Range limited bi-histogram equalization for image contrast enhancement. Optic 2013, 124, 425-431.
- Yu Wang.; Quinchen, BaeominZang. Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Transactions on Consumer Electronics Feb 1999, 45, issue 1.
- Kuldeep, singh. Rajiv, Kapoor. Image Enhancement using Exposure Based Sub Image Histogram Equalization. Pattern Recognition Letter 2014, 36, 10-14.
- Sandeepa K S, Basavaraj N Jagadale, and J S Bhat, "Standard Intensity Deviation Approach based Clipped Sub Image Histogram Equalization Algorithm for Image Enhancement" International Journal of Advanced Computer Science and Applications(IJACSA), 9(1), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090116
- Sandeepa K S, Basavaraj N Jagadale, J S Bhat, Mukund N Naragund and Panchaxri, “Image Contrast Enhancement by Scaling Reconstructed Approximation Coefficients using SVD Combined Masking Technique” International Journal of Advanced Computer Science and Applications(IJACSA), 9(2), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090218
- W. K. Carey, D. B. Chuang, and S. S. Hemami, “Regularity-preserving image interpolation,” IEEE Trans. Image Process., vol. 8, no.9, pp. 1295–1297, Sep. 1999.
- X. Li and M. T. Orchard, “New edge-directed interpolation,” IEEE Trans. Image Process., vol. 10, no. 10, pp. 1521–1527, Oct. 2001.
- K. Kinebuchi, D. D. Muresan, and R. G. Baraniuk, “Wavelet-based statistical signal processing using hidden Markov models,” in Proc. Int. Conf. Acoust., Speech, Signal Process., 2001, vol. 3, pp. 7–11.
- S. Zhao, H. Han, and S. Peng, “Wavelet domain HMT-based image super-resolution," in Proc. IEEE Int. Conf. Image Process., Sep. 2003, vol. 2, pp. 933–936.
- A. Temizel and T. Vlachos, “Wavelet domain image resolution enhancement using cycle-spinning,” Electron. Letter. vol. 41, no. 3, pp. 119–121, Feb. 3, 2005.
- A. Temizel and T. Vlachos, “Image resolution upscaling in the wavelet domain using directional cycle spinning,” J. Electron. Image. vol. 14, no. 4, 2005.
- A .F. D. Araujo, et al., New artificial life model for image enhancement, Expert Syst. Appl.41 (2014)5892–5906.
- Sandeepa K S., B N Jagadale and J S Bhat. A Modified Context based Image Interpolation Algorithm for Digital Images. International Journal of Computer Applications 171(2):34-37, August 2017.
- Bhandaria, A.k; Sonia, V.; H.; Kumaran, A.; Singhb, G.K.Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT–SVD. ISA Transactions 2014, 53, 1286-1296.
- Yang, X.S.; Deb, S. Cuckoo Search via levy flights in proceedings of the world congress on nature and biological inspired computing (NaBIC). IEEE publications, 2009,pp,210-214.
- Shilpha Suresh, Dr.Shyam Lal, “An Efficient Cuckoo Search Algorithm based Multilevel Thresholding for Segmentation of Satellite Images Using Different Objective Functions”, Expert Systems with Applications 58 · April 2016,184-209.
- Iqbal, M.Z.; Ghafoor, A.; Siddiqui, A.M; Riaz, M.M.; Khalid, U.Dual-tree complex wavelet transform and SVD based medical image resolution enhancement. Signal Processing Dec 2014, 105, Pages 430–437.