Abstract
Image is an important carrier of information, but the existence of noise will affect the quality and efficiency of information interaction. Image denoising is a classical problem in image processing, in order to improve the denoising effect, we proposed a method based on improved K-SVD algorithm. First, the high frequency and low frequency of the original noisy image are separated. Besides, the improved K-SVD algorithm is used for sparse reconstruction of the high frequency part of the image. Finally, the denoised high frequency part and the low frequency part are superimposed to make the final clean image. The experiments show that this method can achieve better denoising effect compared with DCT based denoising algorithm, K-SVD algorithm and LC-KSVD model.
This work was supported in part by the National Natural Science Foundation of China under Grant 11862006, Grant 61862025, in part by the Jiangxi Provincial Natural Science Foundation under Grant 2018ACB21032, 20181BAB211016, in part by the Scientific and Technological Research Project of Education Department in Jiangxi Province under Grant GJJ170381, Grant GJJ170383.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, Y., Wang, Z.: Simultaneous image fusion and denoising with adaptive sparse representation. Image Process. IET 9(5), 347–357 (2014)
Guo, D.Q., Yang, H.Y., Liu, D.Q., et al.: Overview on sparse image denoising. Appl. Res. Comput. 29, 406–413 (2012)
Zhang, L., Zhou, W., Chang, P.: Kernel sparse representation-based classifier. IEEE Trans. Signal Process. 60(4), 1684–1695 (2012)
Wang, J., Cai, J.F., Shi, Y., et al.: Incoherent dictionary learning for sparse representation based image denoising. In: IEEE International Conference on Image Processing. IEEE (2015)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation. IEEE Press, Piscataway (2006)
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)
Kong, Y.H., Hu, Q.Y.: An image denoising algorithm via sparse and redundant representations over improved K-singular value decomposition algorithm. Sci. Technol. Eng. 18(1), 287–292 (2018)
Romano, Y., Elad, M.: Improving K-SVD denoising by post-processing its method-noise. In: IEEE International Conference on Image Processing. IEEE (2014)
Shi, J., Wang, X.H.: Image super-resolution reconstruction based on improved K-SVD dictionary-learning. Acta Electron. Sin. 41(5), 997–1000 (2013)
Niu, B., Li, H.Y.: Research on low dictionary coherence K-SVD algorithm. Comput. Digit. Eng. 47(01), 97–103 (2019)
Tan, C., Wei, Z.H., Wu, Z.B., et al.: Parallel optimization of K-SVD algorithm for image denoising based on Spark. In: 2016 IEEE 13th International Conference on Signal Processing (ICSP). IEEE (2017)
Wang, X.Y.: The Research of Sparse Decomposition in the Field of Image Denoising. Anhui University (2014)
Huang, H F. Research on Methods and Applications of Digital Image Sparse Representation. South China University of Technology (2016)
Wu, D., Du, X., Wang, K.Y.: An effective approach for underwater sonar image denoising based on sparse representation, pp. 389–393. https://doi.org/10.1109/icivc.2018.8492877
Jing, C.M., Xiao, L.: An improved image enhancement algorithm. Wuhan Univ. J. Nat. Sci. 22(1), 85–92 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, J. et al. (2019). Image Denoising Based on Sparse Representation over Learned Dictionaries. In: Vaidya, J., Zhang, X., Li, J. (eds) Cyberspace Safety and Security. CSS 2019. Lecture Notes in Computer Science(), vol 11983. Springer, Cham. https://doi.org/10.1007/978-3-030-37352-8_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-37352-8_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37351-1
Online ISBN: 978-3-030-37352-8
eBook Packages: Computer ScienceComputer Science (R0)