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Design Simulation and Assessment of Image De-noising through Improved Median Filters Based Sub band Decomposition

Kanika Shrivastava, Dr.Vikas Soni, Jitendra K. Yadvendra


The quality of an image sequence can degrade due to a number of causes, such as noise in the recording, transmission, or scanning. Additionally to enhancing the visual appeal, filtering offers the advantage of facilitating subsequent image processing tasks like coding, interpretation, or analysis. While attempting to restore a perfect image from a faulty copy, noise reduction is a top concern. Because of this, there isn't a single strategy that can be utilized for everything. However, in terms of performance, linear filters are unrivalled for eliminating Gaussian signal noise. Photographs can show noise. Linear filters are triggered by certain frequencies while the signal and noise are distributed across a wide region. usually in an unsatisfactory way. On the other hand, denoising a picture by breaking it up into several scales is a regular occurrence in the field of image processing, similar to the process of dissecting an item. In other cases, the knowledge of artifacts is more ingrained in the segmented picture during image processing. Here, a brand-new method for denoising images is provided that removes various sorts of noise from an image using sub band decomposition based on a median filter. The wavelet decomposition and median transform are combined for the benefits of sub-band decomposition. In the lab, it has been noted that the noisy coefficients value is higher on the first scale but decreases with time due to multiresolution analysis on the next gradation, and so on. Since this was the case, we came up with the following solution: using multi-resolution coefficients to remove background noise while preserving the quality of the picture, and gradually reducing the threshold for each stage of the process to allow the de-noising operation to filter out the noise. There are a couple of noise-filled, jumbled images. This unique approach reduces noises including gaussian, speckle, and salt-and-pepper noises when compared to earlier methods utilizing the PSNR measure denoising methods based on wavelets. Experiments support the hypothesis. To show that a novel sub band decomposition method based on enhanced symmetric, bell-shaped, and cantered weighted median filters has been proposed. Under various noise conditions and varied performance factors, the method outperforms the traditional wavelet decomposition method.


Image Processing, PSNR, Image Watermarking, Image Enhancement, Discrete Wavelet Transform, Discrete Cosine Transform, LBP, GLCM, PCA

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. 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.


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