A New Hybrid Filter Approach for Image Processing

Year 2020, Volume: 3 Issue: 3, 334 - 342, 30.12.2020

Bekir Aksoy Osamah Khaled Musleh Salman

https://doi.org/10.35377/saucis.03.03.785749

Cited By: 2

Abstract

Today, with the rapidly advancing technology, the importance of image processing techniques is increasing. Image processing is used in many areas from facial recognition to plant disease identification. One of the important image processing stages is the filtering stage used for smoothing images and object detection. Among these filtering techniques, basic filtering techniques such as mean, median and Gaussian are used in image processing. However, these filtering techniques are known to be insufficient to achieve the desired results in some cases. In this study, a new hybrid filtering approach named Mean-Median-Gaussian (MMG) is presented using these three basic filtering techniques. It has been demonstrated that the obtained MMG hybrid algorithm gives more successful results than these three basic filtering techniques in smoothing the images and determining the boundary lines.

Keywords

Image Processing, Filters, Smoothing methods, Drawing Contour, MMG

References

• F. Jalled and I. Voronkov, “Object detection using image processing,” 2016. [Online]. Available: arXiv:1611.07791.
• S. R.Balaji and S. Karthikeyan, “A survey on moving object tracking using image processing,” in Proc. 11th Int. Conf. on Intelligent Syst. and Control (ISCO), Coimbatore, India, Jan. 5-6, 2017, pp. 469-474.
• Q. Chen, J. Xu and V. Koltun, “Fast image processing with fully-convolutional networks,” in Proc. of the IEEE Int. Conf. on Comput. Vision (ICCV), Venice, Italy, Oct. 22-29, 2017, pp. 2497-2506.
• G. Adlinge, S. Kashid, T. Shinde and V. Dhotre, “Text Extraction from image using MSER approach,” Int. J. Res. Eng. Technol. (IRJET), vol. 3, no. 05, 2016.
• P. V. Garad, “Object sorting robot based on the shape,” Int. J. of Adv. Res., Ideas and Innov. in Technol., vol. 3, pp. 129-134, 2017.
• G. M. Perihanoğlu “Feature Extraction From Images By Using Digital Image Processing Techniques,” M.S. thesis, Dept. Geomathic Eng., Istanbul Technical Univ., Turkey, 2015.
• A. M. Galal, “An analytical study on the modern history of digital photography,” Int. J. Des., vol. 26, no. 58, pp. 1-13, 2016.
• X. Deng, Y. Ma and M. Dong, “A new adaptive filtering method for removing salt and pepper noise based on multilayered PCNN,” Pattern Recognit. Lett., vol.79, pp. 8-17, 2016.
• Y. Li and S. Sun, “Research on Suppression Method of Warhead Infrared Image Background Based on Small Area Filtering,” J. Comput. Commun., vol. 6, no. 11, pp. 155-161, 2018.
• W. Ma et al., “Adaptive median filtering algorithm based on divide and conquer and its application in CAPTCHA recognition,” Comput., Mater. & Continua, vol. 58, no. 3, pp. 665-677, 2019.
• H. Michalak and K. Okarma, “Improvement of image binarization methods using image preprocessing with local entropy filtering for alphanumerical character recognition purposes,” Entropy, vol. 21, no. 6, pp. 562, 2019.
• Q. Fan et al., “A generic deep architecture for single image reflection removal and image smoothing,” in Proc. IEEE Int. Conf. on Comput. Vision, Venice, Italy, Oct. 22-29, 2017, pp. 3238-3247.
• W. Liu et al., “Real-time Image Smoothing via Iterative Least Squares,” 2020. [Online]. Available: arXiv:2003.07504.
• Q. Fan et al., “Image smoothing via unsupervised learning,” ACM Trans. on Graph. (TOG), vol. 37, no. 6, pp. 1-14, 2018.
• P. Li, X. Liu and H. Xiao, “Quantum image median filtering in the spatial domain,” Quantum Inf. Process., vol. 17, no. 3, pp. 49, 2018.
• G. George, R. M. Oommen, S. Shelly, S. S. Philipose and A. M. Varghese, “A survey on various median filtering techniques for removal of impulse noise from digital image,” in Proc. 2018 Conf. on Emerg. Devices and Smart Syst. (ICEDSS), Tamilnadu, India, Mar. 2-3, 2018, pp. 235-238.
• P. Zhang and F. Li, “A new adaptive weighted mean filter for removing salt-and-pepper noise,” IEEE Signal Process. Lett., vol. 21, no. 10, pp. 1280-1283, 2014.
• Z. Zhang et al., “A new adaptive switching median filter for impulse noise reduction with pre-detection based on evidential reasoning,” Signal Process., vol. 147, pp. 173-189, 2018.
• S. B. S. Fareed and S. S. Khader, “Fast adaptive and selective mean filter for the removal of high-density salt and pepper noise,” IET Image Process., vol .12, no. 8, pp. 1378-1387, 2018.
• B .Gupta and S. N. Shailendra, “Image Denoising with Linear and Non-Linear Filters: A Review,” Int. J. of Comput. Sci. Issues (IJCSI), vol. 10, no. 6, pp. 149-154, 2013.
• I. Agustina, F. Nasir and A. Setiawan, “The Implementation Of Image Smoothing To Reduce Noise Using Gaussian Filter,” Int. J. of Comput. Appl., vol. 177, no. 5, pp. 15-19, 2017.
• F. Cabello, J. León, Y. Iano and R. Arthur, “Implementation of a fixed-point 2D Gaussian Filter for Image Processing based on FPGA,” in Proc. 2015 Signal Process.: Algorithms, Architectures, Arrangements, and Appl. (SPA), Poznan, Poland, Sept 23-25, 2015, pp. 28-33.
• B. Garg and G. K. Sharma, “A quality-aware Energy-scalable Gaussian Smoothing Filter for image processing applications,” Microprocessors and Microsystems, vol. 45, pp. 1-9, 2016.