Authors : Kalyani Akhade; Sakshi Ghodekar; Vaishnavi Kapse; Anuja Raykar; Sonal Wadhvane

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/42dtbxvu

Scribd : https://tinyurl.com/35yt3pxj

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR1565

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : As digital imaging becomes increasingly important in various fields, the demand for effective methods to reduce image noise has risen. This study explores a wide range of techniques for denoising images, including both traditional and modern methods. It examines classical filters, statistical methods, and contemporary machine learning algorithms, explaining their principles, strengths, and weaknesses. Through a systematic review of existing literature, these techniques are categorized based on their underlying approaches and practical uses. Comparative analyses offer insights into the advantages and drawbacks of each method. Additionally, the paper discusses current trends and future directions in image denoising research. This comprehensive study serves as a valuable resource for researchers, professionals, and enthusiasts seeking a deep understanding of the evolving field of image denoising.

Keywords : Wavelet Transformer, Image Denoising, Machine Learning.

References :

1. T. Brooks, B. Mildenhall, T. Xue, J. Chen, D. Sharlet, and J. T. Barron, “Unprocessing images for learned raw denoising,” in Proc.IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2019,pp. 11036–11045.
2. Y. Wang, H. Huang, Q. Xu, J. Liu, Y. Liu, and J. Wang, “Practical deep raw image denoising on mobile devices,” in Proc. Eur. Conf. Comput.Vis. (ECCV), Aug. 2020, pp. 1–16.
3. C. A. Metzler, A. Maleki, and R. G. Baraniuk, “BM3D-AMP: A new image recovery algorithm based on BM3D denoising,” in Proc. IEEE Int. Conf. Image Process. (ICIP), Sep. 2015, pp. 3116–3120.
4. K. Zhang, W. Zuo, S. Gu, and L. Zhang, “Learning deep CNN denoiser prior for image restoration,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 3929– 3938.
5. K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,”IEEE Trans. Image Process., vol. 26, no. 7, pp. 3142–3155, Jul. 2017.
6. S. Guo, Z. Yan, K. Zhang, W. Zuo, and L. Zhang, “Toward convolutional blind denoising of real photographs,” in Proc. IEEE/CVF Conf. Comput.Vis. Pattern Recognit. (CVPR), Jun. 2019, pp. 1712–1722.
7. T. Blu and F. Luisier, “The SURE-LET approach to image denoising,”IEEE Trans. Image Process., vol. 16, no. 11, pp. 2778–2786, Nov. 2007.
8. M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process.,vol. 15, no. 12, pp. 3736–3745, Dec. 2006.
9. W. Dong, X. Li, L. Zhang, and G. Shi, “Sparsity-based image denoising via dictionary learning and structural clustering,” in Proc. CVPR,Jun. 2011, pp. 457–464.
10. WINNet: Wavelet-Inspired Invertible Network for Image Denoising Jun-Jie Huang , Member, IEEE, and Pier Luigi Dragotti , Fellow, IEEE. IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 31, 2022.