International Journal of New Developments in Engineering and Society, 2019, 3(2); doi: 10.25236/IJNDES.19226.
Weiliang Li1,*, Yuwei Lan2, Yiming Zhou3
1.College of Internet of Things Engineering, HoHai University, Changzhou Jiangsu, 213022, China
2.College of Internet of Things Engineering, HoHai University, Changzhou Jiangsu, 213022, China
3.Hohai University, Changzhou Jiangsu, 213022, China
The traditional median filtering method will lose some of the image details while removing the impulse noise, and the running speed can not meet the real-time requirements well. As an important medium and means of transmitting information, image information is particularly important in human-accepted information. As a nonlinear filtering method, median filtering can eliminate random noise and pulse interference, and can retain the edge information of the image to a large extent. Combining with the block median filtering method under the multi-window structure of automatic recognition, it can preprocess the image well under the complex situation. Digital image processing is mainly realized by computer, which can process image information to meet people's visual psychology or application needs. As a typical non-linear filtering method, median filtering is widely used in digital image processing.
Median filtering; Image information; Automatic recognition; Multi-window
Weiliang Li, Yuwei Lan, Yiming Zhou. Adaptive Median Filtering Algorithm under Multi-windows in Digital Image Processing Based on Automatic Recognition. International Journal of New Developments in Engineering and Society (2019) Vol.3, Issue 2: 192-197. https://doi.org/10.25236/IJNDES.19226.
 Chen C, Ni J, Huang J (2013). Blind Detection of Median Filtering in Digital Images: A Difference Domain Based Approach. IEEE Transactions on Image Processing, vol.22, no.12, p.4699-4710.
 El-Dahshan E S A, Mohsen H M, Revett K, et al (2014). Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Systems with Applications, vol.41, no.11, p.5526-5545.
 Nair M S, Mol P M A (2014). An Efficient Adaptive Weighted Switching Median Filter for Removing High Density Impulse Noise. Journal of The Institution of Engineers (India): Series B, vol.95, no.3, p.255-278.
 Lien C Y, Huang C C, Chen P Y, et al (2013). An Efficient Denoising Architecture for Removal of Impulse Noise in Images. IEEE Transactions on Computers, vol.62, no.4, p.631-643.
 Zhang Y, Li S, Wang S, et al (2014). Revealing the Traces of Median Filtering Using High-Order Local Ternary Patterns. IEEE Signal Processing Letters, vol.21, no.3, p.275-279.
 Matsuoka J, Koga T, Suetake N, et al (2016). Switching non-local vector median filter. Optical Review, vol.23, no.2, p.195-207.
 Gao W, Mogi T, Rong J, et al (2015). Motion behaviors of the unburned particles ahead of flame front in hexadecanol dust explosion. Powder Technology, vol.271, p.125-133.
 Chaudhury S P, Ravishankar T, Ray A K, et al (1998). Towards an Integrated Design of Ranked Order and Median Filter for 2-D Images. IETE Journal of Research, vol.44, no.6, p.267-274.
 Yang Q, Ahuja N, Tan K H (2015). Constant Time Median and Bilateral Filtering. International Journal of Computer Vision, vol.112, no.3, p.307-318.
 Angulo J (2013). Morphological bilateral filtering. Siam Journal on Imaging Sciences, vol.6, no.3, p.1790-1822.