Comprehensive Review and Analysis of Improved Filtering Methodology Based Image De-noising System

Kanika Shrivastava, Vikas Soni, Jitendra K Yadvendra


A digital image is often ruined by noise that comes from the process and equipment used to make it or that is added to the image while it is being sent. Different kinds of noise, like impulse noise, uniform noise, Salt-and-Pepper noise, and additive Gaussian noise, can show up in images. Using a good image de-noising method, noise can be taken out of an image to bring back its details. In this study, we propose and test a new way to remove noise from images by using a median filter (MF) in the wavelet domain. When testing the proposed method, different types of wavelet transform filters are used along with the median filter to get better results for the image de-noising process and, in turn, to find the best filter. Wavelet transform is a powerful way to analyze images. It does this by looking at the frequencies of the sub-bands that are split from an image. Based on these tests, the proposed method works better than using either the wavelet transform or the median filter alone. The MSE and PSNR values are used to measure how much an image has been cleaned up. With more and more digital images being made, we need better ways to remove noise from images. No matter how good a camera is, improving the image is often a good idea to make it more useful.


Image Processing, Image Watermarking, AGN; Noisy image; Median filter (MF); DWT; PSNR; Threshold


. Ramadan, H., Lachqar, C. & Tairi, H. A survey of recent interactive image segmentation methods. Comp. Visual Media 6, 355–384 (2020).

. Rituparna Sarma and Yogesh Kumar Gupta. A comparative study of new and existing segmentation techniques. IOP Conf. Series: Materials Science and Engineering, 1022 (2021) 012027, doi:10.1088/1757-899X/1022/1/012027.

. K. Jeevitha, A. Iyswariya, V. RamKumar, S. Mahaboob Basha , V. Praveen Kumar. A REVIEW ON VARIOUS SEGMENTATION TECHNIQUES IN IMAGE PROCESSSING. European Journal of Molecular & Clinical Medicine ISSN 2515-8260 Volume 7, Issue 4, 2020.

. Zotin, Alexander, Konstantin Simonov, Mikhail Kurako, Yousif Hamad, Svetlana Kirillova. (2018) “Edge detection in MRI brain tumor images based on fuzzy C-means clustering.” Procedia Computer Science 126: 1261–1270.

. S. Yuan, S. E. Venegas-Andraca, C. Zhu, Y. Wang, X. Mao and Y. Luo, "Fast Laplacian of Gaussian Edge Detection Algorithm for Quantum Images," 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS), Shenyang, China, 2019, pp. 798-802.

. Magdalene C. Unajan Magdalene C. Unajan, Member, IAENG, Bobby D. Gerardo, Ruji P. Medina “A Modified Otsu-based Image Segmentation Algorithm (OBISA) “Proceedings of the International MultiConference of Engineers and Computer Scientists 2019 IMECS 2019, March 13-15, 2019, Hong Kong.

. Seemawazarkar, Bettahally N. Keshavamurthy, Ahsan Hussain (2018) ”Region-based segmentation of social images using Soft KNN algorithm”, 6th International conference on smart computing and communications, Procedia computer Science, 125: 93-98.

. Nguyen MongHien, Nguyen ThanhBinh and Ngo Quoc Viet, "Edge detection based on Fuzzy C Means in medical image processing system," 2017 International Conference on System Science and Engineering (ICSSE), Ho Chi Minh City, 2017, pp. 12-15, doi: 10.1109/ICSSE.2017.8030827.

. Patel, Isha & Patel, Sanskruti. (2019). Analysis of Various Image Segmentation Techniques for Flower Images. 6. 1936-1943.

. Luxit Kapoor, Sanjeev Thakur,” A Survey on Brain Tumor Detection Using Image Processing Techniques”, 2017 7th International Conference on Cloud Computing, Data Science & Engineering – Confluence,IEEE 2017,pg. 582-585.

. Chao-Lun Kuo, Shyi-Chyi Cheng, Chih-Lang Lin, Kuei-Fang Hsiao, Shang-Hung Lee,”Texture-based Treatment Prediction by Automatic Liver Tumor Segmentation on Computed Tomography”, 2017 IEEE.

. M. Moghbel, S. Mashohor, R. Mahmud, and M. Iqbal Bin Saripan, “Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring,”EXCLI Journal, vol. 15, pp. 406–423, 2016.

. Kapil Kumar Gupta,Dr. Namrata Dhanda,Dr. Upendra Kumar,” A Comparative Study of Medical Image Segmentation Techniques for Brain Tumor Detection”, 4th International Conference on Computing Communication and Automation (ICCCA),2018 IEEE, pg. 1-4

. Xiaoqiang Ji, Yang Li, Jiezhang Cheng,Yuanhua Yu,MeijiaoWang, “Cell Image Segmentation Based on an Improved Watershed Algorithm”, 8th International Congress on Image and Signal Processing (CISP),IEEE 2015, pg. 433-437

. Priyanka Kamra, Rashmi Vishraj, Kanica, Savita Gupta,” Performance Comparison of Image Segmentation Techniques for Lung Nodule Detection in CT Images”, International Conference on Signal Processing, Computing and Control (ISPCC), IEEE 2015,pg. 302-306.

. Yu, H. S.; Yang, Z. G.; Tan, L.; Wang, Y. N.; Sun, W.; Sun, M. G.; Tang, Y. D. Methods and datasets on semantic segmentation: A review. Neurocomputing Vol. 304, 82–103, 2018.

. Chen, X. J.; Pan, L. J. A survey of graph cuts/graph search based medical image segmentation. IEEE Reviews in Biomedical Engineering Vol. 11, 112–124, 2018.

. Jain, S.; Laxmi, V. Color image segmentation techniques: A survey. In: Proceedings of the International Conference on Microelectronics, Computing & Communication Systems. Lecture Notes in Electrical Engineering, Vol. 453. Nath, V. Ed. Springer Singapore, 189–197, 2017.

. Zhu, H. Y.; Meng, F. M.; Cai, J. F.; Lu, S. J. Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation. Journal of Visual Communication and Image Representation Vol. 34, 12–27, 2016.

. Yao, R.; Lin, G.; Xia, S.; Zhao, J.; Zhou, Y. Video object segmentation and tracking: A survey arXiv preprint arXiv:1904.09172, 2019.

. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

. Vemulapalli, Raviteja, et al. "Gaussian conditional random field network for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

. Chandra, Siddhartha, and Iasonas Kokkinos. "Fast, exact and multi-scale inference for semantic image segmentation with deep gaussian crfs." European conference on computer vision. Springer, Cham, 2016.

. Luo, Ping, et al. "Deep dual learning for semantic image segmentation." Proceedings of the IEEE international conference on computer vision. 2017.

. Yang, Ming-Der, et al. "Semantic segmentation using deep learning with vegetation indices for rice lodging identification in multi-date UAV visible images." Remote Sensing 12.4 (2020): 633.


  • There are currently no refbacks.

Copyright (c) 2022 Journal of Microwave Engineering and Technologies