The week's pick
Reseach Article
Analysis of Different Filter Techniques for Image Denoising
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 52 |
| Year of Publication: 2023 |
| Authors: Aziz Makandar, Shilpa Kaman |
10.5120/ijca2023922647
|
Aziz Makandar, Shilpa Kaman . Analysis of Different Filter Techniques for Image Denoising. International Journal of Computer Applications. 184, 52 ( Mar 2023), 21-28. DOI=10.5120/ijca2023922647
Abstract
Images are very important source in research field as it is easier to convey information through them. There are several resources available to generate high quality images, but presence of noise can degrade these images. Hence image denoising is one of the crucial preprocessing steps in digital image processing. This paper is an attempt to study the effect of different noise types on images and how efficiently denoising techniques can reduce noise. Gaussian noise, poisson noise, salt & pepper noise and speckle noises are the most commonly occurring noise types which are considered to conduct experiments with gray scale images. Denoising techniques applied here are gaussian filter, median filter, wiener filter, bilateral filter, non-local means and bm3d. Results of different noise used on gray scale images compared with the help of quantitative and qualitative performance parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).
References
- Patil J. and Jadhav S., “A Comparative Study of Image Denoising Techniques,” International Journal of Innovative Research in Science, Engineering and Technology, Vol. 2, No. 3, (2013).
- Boyat, Ajay Kumar and Brijendra Kumar Joshi. "A review paper: noise models in digital image processing", arXiv preprint arXiv:1505.03489 (2015).
- Wang, Jung-Hua, Wen-Jeng Liu and Lian-Da Lin.,"Histogram-based fuzzy filter for image restoration", IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) vol. 32, no. 2, (2002), pp. 230–238.
- Patidar P., Gupta M., Srivastava S. and Nagawat A.K., “Image de-noising by various filters for different noise”, International journal of computer applications, Vol. 9, No.4, (2010), pp.45-50.
- Benzarti, Faouzi and Hamid Amiri, "Speckle noise reduction in medical ultrasound images", arXiv preprint arXiv:1305.1344 (2013).
- Kaur T., Sandhu M. and Goel P., “Performance Comparison of Transform Domain for Speckle Reduction in Ultrasound Image”, International Journal of Engineering Research and Application, Vol.2, Issue 1, (2012), pp.184-188.
- F. Luisier, T. Blu and M. Unser, "A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding", IEEE Transactions on Image Processing, vol. 16, no. 3, (2007), pp. 593-606.
- Gupta, R., Yadav, P. and Kumar, S., “Race identification from facial images using statistical techniques”, Journal of Statistics and Management Systems, 20(4), (2017) pp.723-730.
- Ma Jianwei and Gerlind Plonka, "The curvelet transform", IEEE signal processing magazine vol. 27, no. 2, (2010), pp.118-133.
- Pitas I and Anastasios N.V, “Nonlinear digital filters: principles and applications”, vol. 84. Springer Science & Business Media, (2013).
- Feng Q., Shuping T., Chao Xu, and Guang Jin. "BM3D‐GT&AD: an improved BM3D denoising algorithm based on Gaussian threshold and angular distance." IET Image Processing 14, no. 3 (2020), pp. 431-441.
- Dabov, K., Foi, A., Katkovnik V. and Egiazarian K., “Image denoising by sparse 3-D transform-domain collaborative filtering”, IEEE Transactions on image processing, 16(8), (2007), pp.2080-2095.
- Yahya, A.A., Tan, J., Su, B., Hu, M., Wang, Y., Liu, K. and Hadi, A.N., “BM3D image denoising algorithm based on an adaptive filtering”, Multimedia Tools and Applications, vol.79, (2020), pp.20391-20427.
- Wang, Z., Bovik, A.C., Sheikh, H.R. and Simoncelli, E.P., “Image quality assessment: from error visibility to structural similarity”, IEEE transactions on image processing, 13(4), (2004), pp.600-612.
- Fan, L., Zhang, F., Fan, H. and Zhang, C.,” Brief review of image denoising techniques”, Visual Computing for Industry, Biomedicine, and Art, 2(1), (2019), pp.1-12.
- Singh, I. and Neeru, N., “Performance comparison of various image denoising filters under spatial domain”, International Journal of Computer Applications, 96(19), (2014), pp.21-30.
- Makandar A., Mulimani D. and Jevoor M.,”Comparative study of different noise models and effective filtering techniques”, International Journal of Science and Research (IJSR), 3(8), (2014), pp.458-464.
- Gonzalez RC and Woods RE, “Digital image processing”, 3rd edition. Prentice- Hall, Inc, Upper Saddle River (2006).
- Bovick A., “Handbook of Image and Video processing”, Academic press, New York (2000).
- Charles Boncelet and Alan C. Bovik, “Image Noise Models”, Handbook of Image and Video Processing, (2005).
- Salivahanan S., Vallavaraj A. and Gnanapriya C.,“Digital Signal Processing,” Tata Mcgraw- Hill, Vol. 23, NewDelhi (2008).
- G. Deng and L. W. Cahill, "An adaptive Gaussian filter for noise reduction and edge detection", IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, San Francisco, CA, USA, vol.03, (1993) , pp. 1615-1619.
- J. Benesty J. C. and Y. Huang, "Study of the widely linear Wiener filter for noise reduction," IEEE International Conference on Acoustics, Speech and Signal Processing, (2010), pp. 205-208.
- K. N. Chaudhury and K. Rithwik, “Image denoising using optimally weighted bilateral filters: A sure and fast approach,” in IEEE International Conference on Image Processing (ICIP), (2015), pp. 108–112.
- Buades A., "A non-local algorithm for image denoising", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). Computer Vision and Pattern Recognition (2005), Vol. 2, pp. 60–65.
- M. Judson T. Viger and H. Lim, "Efficient and robust non-local means denoising methods for biomedical images", Proceedings of ITM Web Conference, vol. 29, (2019).
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