ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Comparative Study of Median Based Impulse Noise Reduction Methods for Color Images


Affiliations
1 Department of Computer Engineering, National Institute of Technology, Kurukshetra, India
     

   Subscribe/Renew Journal


With the explosion in the number of digital images taken every day, there is a growing demand for more precise and visually appealing images. Images captured by modern cameras, on the other hand, are eventually ruined by noise, which contributes to a reduction in visual image quality. Impulse noise is one of noise as white and black dispersed pixels that can be found in both gray and color images. The impulse noise model is comprised of Salt and Pepper (SAP) noise and random valued impulse noise (RVIN). So far, a lot of impulse denoising methods have been developed for the images (both gray and color). This article provides a comparative study of impulse noise reduction methods applied to color images wherein impulse noise reduction methods are studied with regard to their performance on color images and a thorough comparison is also carried out to cover all of the denoising methods in detail as well as the results they produce. These methods are contrasted with their functionality, relative performance and time complexity. Extensive simulations have been conducted on a set of standard images for performance evaluation of various denoising methods with regard to PSNR, SSIM and NMSE quality metrics.

Keywords

Denoising, Filter, Image, Impulse Noise, Median Filter.
Subscription Login to verify subscription
User
Notifications
Font Size

  • K.N. Plataniotis and A.N. Venetsanopoulos, “Color Image Processing and Applications”, Springer, 2000.

  • R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, 4th Edition, Pearson Education, 2018.

  • J. Astola and P. Kuosmanen, “Fundamentals of Nonlinear Digital Filtering”,5th Edition, CRC Press, 1997.

  • M. Petrou and C. Petrou, “Image Processing: The Fundamentals”, 2nd Edition, John Wiley and Sons, 2010.

  • S.J. Ko and Y.H. Lee, “Center Weighted Median Filters and their Applications to Image Enhancement”, IEEE Transactions on Circuits and Systems, Vol. 38, No. 9, pp. 984-993, 1991.

  • J. Astola, P. Haavisto and Y. Neuvo, “Vector Median Filters”, Proceedings of the IEEE, Vol. 78, No. 4, pp. 678-689, 1990.

  • Y. Dong and S. Xu, “A New Directional Weighted Median Filter for Removal of Random-Valued Impulse Noise”, IEEE Signal Processing Letters, Vol. 14, No. 3, pp. 193-196, 2007.

  • K.K.V. Toh, H. Ibrahim and M.N. Mahyuddin, “Salt-and-Pepper Noise Detection and Reduction using Fuzzy Switching Median Filter”, IEEE Transactions on Consumer Electronics, Vol. 54, No. 4, pp. 1956-1961, 2008.

  • C.C. Kang and W.J. Wang, “Fuzzy Reasoning-Based Directional Median Filter Design”, Signal Processing, Vol. 89, No. 3, pp. 344-351, 2009.

  • K.K.V. Toh and N.A.M. Isa, “Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction”, IEEE Signal Processing Letters, Vol. 17, No. 3, pp. 281-284, 2010.

  • G. Wang, D. Li, W. Pan and Z. Zang, “Modified Switching Median Filter for Impulse Noise Removal”, Signal Processing, Vol. 90, No.5, pp. 3213-3218, 2010.

  • M.S. Nair and G. Raju, “A New Fuzzy-Based Decision Algorithm for High-Density Impulse Noise Removal”, Signal, Image and Video Processing, Vol. 6, No. 4, pp. 579-595, 2012.

  • M.S. Nair and P.M.A. Mol, “Direction based Adaptive Weighted Switching Median Filter for Removing High Density Impulse Noise”, Computers and Electrical Engineering, Vol. 39, pp. 663-689, 2013.

  • L. Jin, C. Xiong and D. Li, “Selective Adaptive Weighted Median Filter”, Optical Engineering, Vol. 47, No. 3, pp. 1-5, 2008.

  • J. Xu, L. Wang and Z. Shi, “A Switching Weighted Vector Median Filter based on Edge Detection”, Signal Processing, Vol. 98, pp. 359-369, 2014.

  • Z. Li, G. Liu, Y. Xu and Y. Cheng, “Modified Directional Weighted Filter for Removal of Salt and Pepper Noise”, Pattern Recognition Letters, Vol. 40, pp. 113-120, 2014.

  • F. Ahmed and S. Das, “Removal of High-Density Salt-and-Pepper Noise in Images with an Iterative Adaptive Fuzzy Filter using Alpha-Trimmed Mean”, IEEE Transactions on Fuzzy Systems, Vol. 22, No. 5, pp. 1352-1358, 2014.

  • X. Lan and Z. Zuo, “Random-Valued Impulse Noise Removal by the Adaptive Switching Median Detectors and Detail-Preserving Regularization”, Optik, Vol. 125, pp. 1101-1105, 2014.

  • G. Wang, H. Zhu and Y. Wang, “Fuzzy Decision Filter for Color Images Denoising”, OPTIK, Vol. 126, pp. 2428-2432, 2015.

  • J.Y. Chang and P.C. Liu, “A Fuzzy Weighted Mean Aggregation Algorithm for Color Image Impulse Noise Removal”, Proceedings of IEEE International Conference on Automation Science and Engineering, pp. 1268-1273, 2015.

  • M. Habib, A. Hussain, S. Rasheed, and M. Ali, “Adaptive Fuzzy Inference System based Directional Median Filter for Impulse Noise Removal”, International Journal of Electronics and Communications, Vol. 70, No. 5, pp. 689-697, 2016.

  • L. Jin, Z. Zhu, X. Xu and X. Li, “Two-Stage Quaternion Switching Vector Filter for Color Impulse Noise Removal”, Signal Processing, Vol. 128, pp. 171-185, 2016.

  • C.C. Hung and E.S. Chang, “Moran’s I for Impulse Noise Detection and Removal in Color Images”, Journal of Electronic Imaging, Vol. 26, No. 2, pp. 1-20, 2017.

  • A. Roy, J. Singha, L. Manam,and R.H. Laskar, “Combination of Adaptive Vector Median Filter and Weighted Mean Filter for Removal Of High Density Impulse Noise from Color Images”, IET Image Processing, Vol. 11, No. 6, pp. 352-361, 2017.

  • Z. Zhu, L. Jin, E. Song and C.C. Hung, “Quaternion Switching Vector Median Filter Based on Local Reachability Density”, IEEE Signal Processing Letters, Vol. 25, No. 6, pp. 843-847, 2018.

  • A. Roy, L. Manam and R.H. Laskar, “Region Adaptive Fuzzy Filter: An Approach for Removal of Random-Valued Impulse Noise”, IEEE Transactions on Industrial Electronics, Vol. 65, No. 9, pp. 7268-7278, 2018.

  • U. Erkan, L. Gokrem and S. Enginoglu, “Different Applied Median Filter in Salt and Pepper Noise”, Computers and Electrical Engineering, Vol. 70, pp. 789-798, 2018.

  • U. Erkan and L. Gokrem, “A New Method Based on Pixel Density in Salt and Pepper Noise Removal”, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 26, No. 1, pp. 162-171, 2018.

  • J. Chen, Y. Zhan and H. Cao, “Adaptive Sequentially Weighted Median Filter for Image Highly Corrupted by Impulse Noise”, IEEE Access, Vol. 7, pp. 158545-158556, 2020.

  • D.N.H. Thanh, N.H. Hai, V.B.S. Prasath, L.M. Hieu and J.M.R.S. Tavares, “A Two-Stage Filter for High Density Salt and Pepper Denoising”, Multimedia Tools and Applications, Vol. 79, pp. 21013-21035, 2020.

  • A.Q.M. Taha and H. Ibrahim, “Reduction of Salt-and-Pepper Noise from Digital Grayscale Image by using Recursive Switching Adaptive Median Filter”, Proceedings of International Symposium on Intelligent Manufacturing and Mechatronics, pp. 32-47, 2020.

  • A. Roy and R.H. Laskar, “Multiclass SVM based Adaptive Filter for Removal of High Density Impulse Noise from Color Images”, Applied Soft Computing, Vol. 46, pp. 816-826, 2016.

  • L. Jin, Z. Zhu, E. Song and X. Xu, “An Effective Vector Filter for Impulse Noise Reduction based on Adaptive Quaternion Color Distance Mechanism”, Signal Processing, Vol. 155, pp. 334-345, 2019.

  • L. Malinski and B. Smolka, “Fast Averaging Peer Group Filter for the Impulsive Noise Removal in Color Images”, Journal of Real-Time Image Processing, Vol. 11, No. 3, pp. 427-444, 2016.

  • K. Radlak, L. Malinski and B. Smolka, “Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images”, Sensors, Vol. 20, pp. 1-23, 2020.

  • A. Noor, Y. Zhao, R. Khan, L. Wu and F.Y.O. Abdalla, “Median Filters Combined with Denoising Convolutional Neural Network for Gaussian and Impulse Noises”, Multimedia Tools and Applications, Vol. 79, pp. 18553-18568, 2020.

  • M. Ashpreet and M. Biswas, “Impulse Noise Detection and Removal Method Based on Modified Weighted Median”, International Journal of Software Innovation, Vol. 8, No. 2, pp. 38-53, 2020.

  • M. Ashpreet and M. Biswas, “Modified Directional and Fuzzy Based Median Filter for Salt-and-Pepper Noise Reduction in Color Image”, Solid State Technology, Vol. 63, No. 5, pp. 4033-4053, 2020.

  • S. Schulte, V.D. Witte, M. Nachtegael, D.V. Weken and E.E. Kerre, “Histogram-Based Fuzzy Colour Filter for Image Restoration”, Image and Vision Computing, Vol. 25, No. 9, pp. 1377-1390, 2007.

  • M.E. Celebi and Y.A. Aslandogan, “Robust Switching Vector Median Filter for Impulsive Noise Removal”, Journal of Electronic Imaging, Vol. 17, No. 4, pp. 1-10, 2008.

  • A. Pattnaik, S. Agarwal and S. Chand, “A New and Efficient Method for Removal of High Density SPN through Cascade Decision-Based Filtering Algorithm”, Procedia Technology, Vol. 6, pp. 108-117, 2012.

  • S. Masood, A. Hussain, M.A. Jaffar and T.S. Choi, “Color Difference based Fuzzy Filter for Extremely Corrupted Color IMAGES”, Applied Soft Computing, Vol. 21, pp. 107-118, 2014.

  • M. Habib, A. Hussain and T.S. Choi, “Adaptive Threshold based Fuzzy Directional Filter using Background Information”, Applied Soft Computing, Vol. 29, pp. 471-478, 2015.

  • J. Matsuoka, T. Koga, N. Suetake and E. Uchino, “Switching Non-Local Vector Median Filter”, Optical Review, Vol. 23, No. 2, pp. 195-207, 2016.

  • U. Erkan and A. Kilicman, “Two New Methods for Removing Salt-and-Pepper Noise from Digital Images”, Science Asia, Vol. 42, pp. 28-32, 2016.

  • B. Roig and V.D. Estruch, “Localised Rank-Ordered Differences Vector Filter for Suppression of High-Density Impulse Noise in Colour Images”, IET Image Processing, Vol. 10, No. 1, pp. 24-33, 2016.

  • L. Malinski and B. Smolka, “Fast Adaptive Switching Technique of Impulsive Noise Removal in Color Images”, Journal of Real-Time Image Processing, Vol. 16, No. 4, pp. 1077-1098, 2019.

  • J. Chen, Y. Zhan and H. Cao, “Iterative Deviation Filter for Fixed-Valued Impulse Noise Removal”, Multimedia Tools and Applications, Vol. 79, pp. 23695-23710, 2020.


Abstract Views: 383

PDF Views: 1




  • A Comparative Study of Median Based Impulse Noise Reduction Methods for Color Images

Abstract Views: 383  |  PDF Views: 1

Authors

Ashpreet
Department of Computer Engineering, National Institute of Technology, Kurukshetra, India
Mantosh Biswas
Department of Computer Engineering, National Institute of Technology, Kurukshetra, India

Abstract


With the explosion in the number of digital images taken every day, there is a growing demand for more precise and visually appealing images. Images captured by modern cameras, on the other hand, are eventually ruined by noise, which contributes to a reduction in visual image quality. Impulse noise is one of noise as white and black dispersed pixels that can be found in both gray and color images. The impulse noise model is comprised of Salt and Pepper (SAP) noise and random valued impulse noise (RVIN). So far, a lot of impulse denoising methods have been developed for the images (both gray and color). This article provides a comparative study of impulse noise reduction methods applied to color images wherein impulse noise reduction methods are studied with regard to their performance on color images and a thorough comparison is also carried out to cover all of the denoising methods in detail as well as the results they produce. These methods are contrasted with their functionality, relative performance and time complexity. Extensive simulations have been conducted on a set of standard images for performance evaluation of various denoising methods with regard to PSNR, SSIM and NMSE quality metrics.

Keywords


Denoising, Filter, Image, Impulse Noise, Median Filter.

References