Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Reference :

    1. Tian, C., Xu, Y., Zuo, ”Image denoising using deep CNN with batch renormalization,” Neural Networks,121, 461–473,2020.
    2. Chunwei Tian, Yong Xu, Zuoyong Li, Wangmeng Zuo, Lunke Fei, Hong Liu, ”Attention-guided CNN for image de-noising,” Neural Networks,124, 117– 129,2020.
    3. Peng, Y., Zhang, L., Liu, S., Wu, X., Zhang, Y., Wang, X, “Dilated residual networks with symmetric skip connection for image denoising,” Neurocomputing, 345, 67–76,2019.
    4. Chuanhui Shana, Xirong Guob, and Jun Oua, “Residual learning of deep convolutional neural networks for image denoising,” Journal of Intelligent Fuzzy Systems, 37, 2809–2818,2019.
    5. Rini Smita Thakur , Ram Narayan Yadav, Lalita Gupta, “State-of-art analysis of image denoising methods using convolutional neuralnetworks,” IET Image Processing, vol. 13 Iss. 13, pp. 2367- 2380,2019.
    6. Sridhar, “Digital image processing” Oxford Publications,New Delhi, India, 2016, 2nd edn., pp. 1– 7.
    7. Zha, Z., Liu, X., Huang, X., Shi, H., Xu, Y., Wang, Q., et al, “Analyzing the group sparsity based on the rank minimization methods,” In 2017 IEEE international conference on multimedia and expo (pp. 883–888). IEEE,2017.
    8.  Mairal, J., Bach, F. R., Ponce, J., Sapiro, G., Zisserman,A, “Non-local sparse models for image restoration,” In ICCV, Vol. 29 (pp. 54–62). Citeseer,2009.
    9. Malfait, M., Roose, “Wavelet-based image denoising using a Markov random field a priori model,” IEEE Transactions on image processing, 6(4), 549– 565,1997.
    10. Chambolle, A. “An algorithm for total variation minimization and applications,” Journal of Mathematical imaging and vision, 20(1–2), 89–97,2004.
    11. Danielyan, A., Katkovnik, V., Egiazarian, K.: “BM3D frames and variational image deblurring”, IEEE Trans. Image Pro- cess., 2012, 21, (4), pp. 1715–1728.
    12. S. Gu , L. Zhang , W. Zuo , X. Feng, “Weighted nuclear norm minimization with application to image denoising,” in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2862–2869.
    13. Beck, A., Teboulle, M., “Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems,” IEEE

Recent Article