Abstract
The image restoration has emerged as a very vital investigation technique in the domain of the image processing. The underlying motive behind the image restoration is devoted to the augmentation of the perceived visual impact of image so as to make it almost identical to the original image. A host of exploration approaches are now in vogues which are intended to steer clear of the noise, thereby regaining the images with original quality. In our earlier research, two distinct noise elimination methods like the (OGHP) and SURE shrinkage were effectively employed for the purpose of denoising, though the relative PSNR and SSIM efficiencies did not come up to the desired level. In the innovative approach envisaged in the document, at the outset, the noise is included by means of two processes like the salt and pepper and impulse noise. Subsequently, the pre-processing methods are performed with the able assistance of two novel filters such as the adaptive median filter and adaptive fuzzy switching. Thereafter, the preprocessed image is furnished to the succeeding function of noise elimination like the (OGHP) and SURE shrinkage. In the course of the OGHP noise elimination technique, the GHP constraints are optimized by employing the Cuckoo Search Algorithm. Thereafter, the noise-eliminated image is effectively estimated with the help of the Discrete Wavelet Transform (DWT). The consequential noiseless images are subjected to the image restoration procedure by efficiently employing the AGA approach. The cheering performance outcomes chant the success stories of the novel image restoration method, highlighting its superlative efficiency. Moreover, the efficacy of the innovative approach is assessed by means of a set of noise-polluted images and contrasted with the modern noiseless image restoration technique.
Similar content being viewed by others
References
Sarker, S., Chowdhury, S., Laha, S., and Dey, D., Use Of Non-Local Means Filter To Denoise Image Corrupted By Salt And Pepper Noise. Signal & Image Processing: An International Journal 3(2):223–235, 2012.
Cho, T. S., Zitnick, L., Joshi, N., Kang, S. B., Szeliski, R., and Freeman, W., Image Restoration by Matching Gradient Distributions. IEEE Trans. Pattern Anal. Mach. Intell. 34(4):683–694, 2012.
Kaur, M., and Sharm, R., Restoration Of Medical Images Using Denoising. International Journal For Science And Emerging Technologies With Latest Trends 5(1):35–38, 2013.
George, A., Rajakumar, B. R., and Suresh, B., Markov Random Field based Image Restoration with aid of Local and Global Features. Int. J. Comput. Appl. 48(8):0975–0888, 2012.
Lefkimmiatis, S., Bourquard, A., and Unser, M., Hessian-Based Norm Regularization For Image restoration With Biomedical Applications. IEEE Trans. Image Process. 21(3):983–995, 2012.
Zheng, S., Pan, Z., Zhao, X., and Wang, G., A General Adaptive Variational Model For Image Restoration Based On First And Second Order Derivatives. Journal Of Computational Information Systems 8(24):10169–10175, 2012.
Sakthidasan @ Sankaran, K., Prabha S., and Rubesh Anand, P. M., Optimized gradient histogram preservation with block wise SURE shrinkage for noise free image restoration. Springer -Cluster Computing- The Journal of Networks, Software Tools and Applications, 1-22 , ISSN 1573-7543, 2018
Zhang, H., Yang, J., Zhang, Y., and Huang, T., Image and Video Restoration via Non-Local Kernel regression. IEEE Transactions on Systems, Man, and Cybernetics–Part B: Cybernetics 42(6):1–12, 2012.
Lopez-Rubio, E., Restoration of images corrupted by Gaussian and uniform impulsive noise. Pattern Recogn. 43(5):1835–1846, 2010.
Yang, L., Parton, R., Ball, G., Qiu, Z., Greenaway, A., Davis, I., and Lu, W., An adaptive non-local means filter for denoising live-cell images and improving particle detection. J. Struct. Biol. 172(3):233–243, 2010.
Lin, T.-C., Decision-based fuzzy image restoration for noise reduction based on evidence theory. Expert Syst. Appl. 38(7):8303–8310, 2011.
Lee, C., Lee, C., and Kim, C.-S., An MMSE approach to nonlocal image denoising: Theory and practical implementation. J. Vis. Commun. Image Represent. 23(3):476–490, 2012.
Zhang, H., Yang, J., Zhang, Y., and Huang, T. S., Image and Video Restorations via Nonlocal Kernel Regression. IEEE Transactions on Cybernetics 43(3):1035–1046, 2013.
Wang, S., Xia, Y., and QiegenLiu, P. D., David Dagan Feng, and Jianhua Luo, Fenchel Duality Based Dictionary Learning for Restoration of Noisy Images. IEEE Trans. Image Process. 22(12):5214–5225, 2013.
Dong, W., Zhang, L., Shi, G., and Li, X., Nonlocally Centralized Sparse Representation for Image Restoration. IEEE Trans. Image Process. 22(4):1620–1630, 2013.
Rasti, B., Sveinsson, J. R., and Ulfarsson, M. O., Wavelet-Based Sparse Reduced-Rank Regression for Hyperspectral Image Restoration. IEEE Trans. Geosci. Remote Sens. 52(10):6688–6698, 2014.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the Topical Collection on Image & Signal Processing
Rights and permissions
About this article
Cite this article
Sakthidasan @ Sankaran K, Vasudevan N, Kumara Guru Diderot P. et al. Efficient Image De-Noising Technique Based on Modified Cuckoo Search Algorithm. J Med Syst 43, 307 (2019). https://doi.org/10.1007/s10916-019-1423-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-019-1423-1