An Image Denoising Scheme Remove Unwanted Pixel Using NLM with Sprint Deep Learning Network

Authors

  • D. Hemanand Professor, Department of Computer Science and Engineering, S.A. Engineering College (Autonomous) Thiruverkadu, Chennai-600077, Tamil Nadu, India
  • L. Selvam Professor and Head, Department of Information Technology, KSR Institute for Engineering and Technology, Tiruchengode-637215, Tamil Nadu, India
  • M. Arunachalam Professor, Department of Information technology, Sri Krishna College of Engineering and Technology, Kuniamuthur, Coimbatore 641008, Tamil Nadu, India
  • D. Suresh Professor, Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul-624622, Tamil Nadu, India

Keywords:

Image noise, Machine learning, Deep Learning networks, non-local means algorithm, Image classification, Segmentation

Abstract

An automatic image processing system is vital for detecting life-threatening diseases like tumor diagnosis. Machine learning is significant in processing medical images to detect the diseases' signs and symptoms. The essential need for image processing is the earlier detection of diseases. But due to the image complexities, the inefficiency of the conventional methods delivers poor performance in detecting the damaged cell's shape, extraction, exact size, and location. The primary goal of achieving an enhanced automatic system for preprocessing, classifying, segmenting, detecting, and sample recognition remains a challenging task. To overcome this, we proposed an improved SPRINT algorithm with NLM (Non-Local Mean filtering) algorithm. The proposed mechanism computes the original images to remove the noises, and the non-local filtering algorithm considers the high-extent redundancy of the normal images. As a result, the input images are processed using the weighted average value of the entire pixel to obtain noise-free or noise-less pixel images. Additionally, the SPRINT algorithm applies the minimum description length principle for achieving accuracy in the expected details. It contains an attribute table and histogram for holding the indexing of data records, class identification, and attribute values. Finally, our enhanced SPRINT can solve this trouble by preserving the fine details of an image while denoising. To evaluate the performance of the proposed system, a comparison work is carried out between the enhanced SPRINT algorithm with a conventional neural network (CNN) [12] and deep learning-based patch label denoising methods (LossDiff) [13]. The proposed SPRINT algorithm achieves 97% accuracy, which is far better than the CNN with 91% and LossDiff with 85% accuracy.

Downloads

Download data is not yet available.

References

Pang, T., Zheng, H., Quan, Y., Ji, H.: Recorrupted-to-recorrupted: unsupervised deep learning for image denoising. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2043–2052 (2021). IEEE

Jo, Y., Chun, S.Y., Choi, J.: Rethinking deep image prior for denoising. In: IEEE Conference on Computer Vision (ICCV), pp. 5087–5096 (2021). IEEE

Bhat, G., Danelljan, M., Yu, F., Van Gool, L., Timofte, R.: Deep reparametrization of multi-frame super-resolution and denoising. In: IEEE Conference on Computer Vision (ICCV), pp. 2460–2470 (2021). IEEE

S. Mirjalili, J. Song Dong, A. S. Sadiq, and H. Faris, “Genetic algorithm: theory, literature review, and application in image reconstruction,” in Nature-Inspired Optimizers. Studies in Computational Intelligence, S. Mirjalili, D. J. Song, and A. Lewis, Eds., Vol. 811, Springer International Publishing, NY, USA, 2020

Mou, C., Zhang, J., Wu, Z.: Dynamic attentive graph learning for image restoration. In: IEEE Conference on Computer Vision (ICCV), pp. 4328– 4337 (2021). IEEE

S, R. D., L. . Shyamala, and S. . Saraswathi. “Adaptive Learning Based Whale Optimization and Convolutional Neural Network Algorithm for Distributed Denial of Service Attack Detection in Software Defined Network Environment”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 6, June 2022, pp. 80-93, doi:10.17762/ijritcc.v10i6.5557.

Wen, J., Xu, Y., & Liu, H. (2020). Incomplete multiview spectral clustering with adaptive graph learning. IEEE Transactions on Cybernetics, 50(4), 1418–1429.

Deep learning for image denoising: a survey. In International conference on genetic and evolutionary computing (pp. 563–572). Springer. Tian, C., Xu, Y., Li, Z., Zuo, W., Fei, L., & Liu, H. (2020).

Attention-guided cnn for image denoising. Neural Networks. Tian, C., Xu, Y., & Zuo, W. (2020). Image denoising using deep cnn with batch renormalization. Neural Networks, 121, 461–473

Gehlot, S., Gupta, A. & Gupta, R. A CNN-based unified framework utilizing projection loss in unison with label noise handling for multiple myeloma cancer diagnosis. Med. Image Anal. 72, 102099 (2021).

Karimi, D., Dou, H., Warfield, S. K. & Gholipour, A. Deep Learning with Noisy Labels: Exploring Techniques and Remedies in Medical Image Analysis. arXiv:191202911 Cs Less Stat (2020).

Zhang, Y., Wang, C., Wang, X., Zeng, W. & Liu, W. A Simple Baseline for Multi-Object Tracking. arXiv:200401888 Cs (2020).

S. Ghose, N. Singh and P. Singh, "Image Denoising using Deep Learning: Convolutional Neural Network," 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2020, pp. 511-517, doi: 10.1109/Confluence4 7617. 2020.9057895.

Garg, D. K. . (2022). Understanding the Purpose of Object Detection, Models to Detect Objects, Application Use and Benefits. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 01–04. https://doi.org/10.17762/ijfrcsce.v8i2.2066

Murtaza Ashraf,Willmer Rafell Quiñones Robles,Mujin Kim,Young Sin Ko,and Mun Yong Yi, "A loss-based patch label denoising method for improving whole-slide image analysis using a convolutional neural network", Sci Rep. 2022; 12: 1392. Published online 2022 Jan 26. doi: 10.1038/s41598-022-05001-8

Chunwei Tian, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, Chia-Wen Lin, "Deep learning on image denoising: An overview", 2020 Elsevier Ltd, https://doi.org/10.1016/ j.neunet. 2020.07.025

Saeed Izadi, Darren Sutton and Ghassan Hamarneh, "Saeed Izadi, Darren Sutton and Ghassan Hamarneh", Springer Nature 2022

Cheng, S., Wang, Y., Huang, H., Liu, D., Fan, H., Liu, S.: Nbnet: Noise basis learning for image denoising with subspace projection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4896–4906 (2021). IEEE

Hu, X., Ma, R., Liu, Z., Cai, Y., Zhao, X., Zhang, Y., Wang, H.: Pseudo 3d auto-correlation network for real image denoising. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16175–16184 (2021). IEEE

Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H., Shao, L.: Multi-stage progressive image restoration. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14821–14831 (2021). IEEE

Liu, Y., Qin, Z., Anwar, S., Ji, P., Kim, D., Caldwell, S., Gedeon, T.: Invertible denoising network: A light solution for real noise removal. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13365–13374 (2021). IEEE

Li, Y., Fu, X., Zha, Z.-J.: Cross-patch graph convolutional network for image denoising. In: IEEE Conference on Computer Vision (ICCV), pp. 4651–4660 (2021). IEEE

Kiran, M. S., & Yunusova, P. (2022). Tree-Seed Programming for Modelling of Turkey Electricity Energy Demand. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 142–152. https://doi.org/10.18201/ijisae.2022.278

SPRINT structure

Downloads

Published

16.12.2022

How to Cite

Hemanand, D. ., Selvam, L., Arunachalam, M., & Suresh, D. (2022). An Image Denoising Scheme Remove Unwanted Pixel Using NLM with Sprint Deep Learning Network. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 130–137. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2207

Issue

Vol. 10 No. 4 (2022)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.