### A Multistage Hybrid Median Filter Design of Stereo Matching Algorithms on Image Processing

#### Abstract

This paper presents a new method of stereo matching algorithms known as a Multistage Hybrid Median Filter (MHMF). The main challenging problem within computer vision field is stereo matching, considering the many drawbacks and issues resulting from stereo matching, which makes it a difficult and unresolved approach. Large studies and methods have been introduced and attempted to deal with stereo matching problems from a different perspective. However, most of these existing methods still suffer from low accuracy and algorithms complexity. Thus, to provide valuable and efficient solutions to stereovision community and to support researchers in the same line of research, we created a highly efficient and robust algorithms based on Hybrid Median Filter (MHF). In this paper, the (MHMF) is introduced as a newly implemented method of stereo matching algorithms. This method consists of three main stages, in which each stage involves multiple processes. In stage 1, the Basic Block Matching (BBM), Dynamic Programming (DP) algorithms are accomplished. While stages 2 and 3 rely on newly developed post-processing algorithms, which involve Hybrid Median Filtering (MHF), segmentation and merging processes as the main core of our research approach. The significant contribution of this method is on its capability of solving major drawbacks and problems of stereo marching including the high noises, horizontal stripes, multiple unwanted regions and aspects, and occlusions problems. The paper provides an evaluation of our method with some existing algorithms using the most common stereo functions including the MSE, PSNR, and SSIM. Finally, it is found that our developed algorithms achieved high accuracy of disparity depth map with convenient execution time among other compared methods.

#### Keywords

#### Full Text:

PDF#### References

S. Zhu and L. Yan, “Local Stereo Matching Algorithm with Efficient Matching Cost and Adaptive Guided Image Filter,” Vis. Comput., vol. 33.9, pp. 1087–1102, 2017.

G. Hong and B. Kim, “A Local Stereo Matching Algorithm Based on Weighted Guided Image Filtering for Improving the Generation of Depth Range Images,” Displays, vol. 49, pp. 80–87, 2017.

M. Aboali, N. A. Manap, A. M. Darsono, and Z. M. Yusof, “Review on Three-Dimensional ( 3-D ) Acquisition and Range Imaging Techniques,” Int. J. Appl. Eng. Res., vol. 12.10, pp. 2409–2421, 2017.

S. Hussain and R. Modi, “Advancement in Depth Estimation for Stereo Image Pair,” Int. J. Innov. Res. Comput. Commun. Eng. 1.4, 2013.

I. Lee and B. Moon, “An Improved Stereo Matching Algorithm with Robustness to Noise Based on Adaptive Support Weight,” J. Inf. Process. Syst., vol. 13.2, pp. 256–267, 2017.

R. Dieny, J. Thevenon, J. Martinez-del-rincon, and J. Nebel, “Bioinformatics Inspired Algorithm for Stereo Correspondence,” 2011.

D. Scharstein and R. Szeliski, “A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms,” nternational J. Comput. Vis., vol. 47.1-3, pp. 7–42, 2001.

A. Hosni, M. Bleyer, and M. Gelautz, “Secrets of adaptive support weight techniques for local stereo matching q,” Comput. Vis. Image Underst., vol. 117, no. 6, pp. 620–632, 2013.

C. Rhemann, A. Hosni, M. Bleyer, C. Rother, and M. Gelautz, “Fast Cost-Volume Filtering for Visual Correspondence and Beyond,” IEEE Trans. Pattern Anal. Mach. Intell. 35.2 504-511, 2013.

L. De-maeztu, S. Mattoccia, and R. Cabeza, “Linear stereo matching,” Comput. Vis. (ICCV), 2011 IEEE Int. Conf. on. IEEE, 2011.

N. Komodakis, G. Tziritas, and N. Paragios, “Fast , Approximately Optimal Solutions for Single and Dynamic MRFs,” Comput. Vis. Pattern Recognition, 2007. CVPR’07. IEEE Conf. on. IEEE, 2007.

J. Sun, N. Zheng, and S. Member, “Stereo Matching Using Belief Propagation,” IEEE Trans. pattern Anal. Mach. Intell. 25.7 787-800. 2003, 2003.

B. Potetz, “Efficient Belief Propagation for Vision Using Linear Constraint Nodes,” Comput. Vis. Pattern Recognition, 2007. CVPR’07. IEEE Conf. on. IEEE, 2007.

L. Torresani, V. Kolmogorov, and C. Rother, “A dual decomposition approach to feature correspondence,” IEEE Trans. pattern Anal. Mach. Intell. 35.2 259-271, 2013.

L. Wang, H. Q., Wu, M., Zhang, Y. B., & Zhang, “Effective stereo matching using reliable points based graph cut,” Vis. Commun. Image Process. (VCIP), 2013, IEEE, 2013.

A. F. Bobick and S. S. Intille, “Large Occlusion Stereo,” Int. J. Comput. Vis., vol. 33.3, pp. 181–200, 1999.

D. Chen, M. Ardabilian, L. Chen, and S. Member, “A Fast Trilateral Filter-Based Adaptive Support Weight Method for Stereo Matching,” IEEE Trans. Circuits Syst. Video Technol., vol. 25.5, pp. 730–743, 2015.

B. Khomutenko and P. Martinet, “Direct Fisheye Stereo Correspondence Using Enhanced Unified Camera Model and Semi-Global Matching Algorithm,” Control. Autom. Robot. Vis. (ICARCV), 2016 14th Int. Conf. on. IEEE, 2016.

J. V. C. I. R, R. Affendi, H. Ibrahim, A. Hasni, and A. Hassan, “Stereo matching algorithm based on per pixel difference adjustment , iterative guided filter and graph segmentation q,” J. Vis. Commun. Image Represent., vol. 42, pp. 145–160, 2017.

J. Menant, G. Gautier, M. Pressigout, L. Morin, and J. Nezan, “An Automatized Method to Parameterize Embedded Stereo Matching Algorithms,” J. Syst. Archit., vol. 80, pp. 92–103, 2017.

D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” Int. J. Comput. Vis. 47.1-3 7-42, 2002.

S. Choi, J. Jeong, and D. H. Hwang, “Post-Processing Algorithms for Real-time Active Stereo Vision,” Consum. Electron. (ISCE 2014), 18th IEEE Int. Symp. on. IEEE, 2014.

X. Wang, Y. Tian, H. Wang, and Y. Zhang, “Stereo Matching by Filtering-Based Disparity Propagation,” PLoS One, vol. 11.9, p. e0162939, 2016.

S. Choi, J. Jeong, J. Chang, and H. Shin, “Implementation of Real-Time Post-Processing for High-Quality Stereo Vision,” ETRI J., vol. 37.4, pp. 752–765, 2015.

P. Fua, I. Sophia-antipolis, R. Lucioles, and F.-V. Cedex, “A parallel stereo algorithm that produces dense depth maps and preserves image features,” Mach. Vis. Appl., vol. 6.1, pp. 35–49, 1993.

T. C. Birchfield S, “A pixel dissimilarity measure that is insensitive to image sampling,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20.4, pp. 401–406, 1998.

M. Camplani, L. Salgado, M. Camplani, and L. Salgado, “Efficient Spatio-Temporal Hole Filling Strategy for Kinect Depth Maps,” Three-Dimensional Image Process. Appl. 8290, 2012.

B. P. Zhou X, “Radiometric invariant stereo matching based on relative gradients,” Image Process. (ICIP), 2012 19th IEEE Int. Conf. (pp. 2989-2992). IEEE, 2012.

C. Lin, C. Varekamp, K. Hinnen, and G. De Haan, “Interactive Disparity Map Post-processing,” Image Process. (ICIP), 2012 19th IEEE Int. Conf. on. IEEE, 2012.

D. Min, J. Oh, and K. Sohn, “Asymmetric post-processing for stereo correspondence,” Pattern Recognition, 2008. ICPR 2008. 19th Int. Conf. on. IEEE, 2008.

D. Maier and M. Reinhard, “Calculating Dense Disparity Maps from Color Stereo Images , an Efficient Implementation,” Int. J. Comput. Vis. 47.1-3 79-88, 2002.

X. Sun, X. Mei, S. Jiao, M. Zhou, and H. Wang, “Stereo Matching with Reliable Disparity Propagation,” 3D Imaging, Model. Process. Vis. Transm. (3DIMPVT), 2011 Int. Conf. on. IEEE, 2011.

Q. Yang, “A Non-Local Cost Aggregation Method for Stereo Matching,” Comput. Vis. Pattern Recognit. (CVPR), 2012 IEEE Conf. (pp. 1402-1409). IEEE, 2012.

H. Komatsu, “Technical Bulletin Interferometry : Principles and Applications of Two-Beam and Multiple-,” p. 26, 1991.

T. C. Huat, A. Manap, and M. Ibrahim, “Development of Double Stage Filter ( DSF ) for Stereo Matching Algorithms and 3D Vision Applications,” J. Telecommun. Electron. Comput. Eng., vol. 7.2, pp. 39–46, 2015.

S. Park, M. Park, and K. Yoon, “Confidence-based Weighted Median Filter for Effective Disparity Map Refinement,” Ubiquitous Robot. Ambient Intell. (URAI), 2015 12th Int. Conf. on. IEEE, 2015.

Q. Yang, “Stereo Matching Using Tree Filtering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37.4, pp. 834–846, 2015.

N. Lazaros, G. C. Sirakoulis, and A. Gasteratos, “Review of Stereo Vision Algorithms : From Software to Hardware,” J. Real-Time Image Process. 11.1 5-25, 2008.

K. Yoon and S. Member, “Adaptive Support-Weight Approach for Correspondence Search æ,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28.4, pp. 650–656, 2006.

W. H. . Huat, T.C., bin Abd Manap, N. and Saad, “Analysis on Segment-Based Double Stage Filter Algorithm for Stereo Matching,” J. Eng. Appl. Sci., vol. 11, no. 10, pp. 6240–6245, 2016.

N. Cross-correlation, “Robust Stereo Matching Using Adaptive,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, pp. 807–822, 2011.

Y. Wang, C. Tung, and P. Chung, “Efficient Disparity Estimation Using Hierarchical Bilateral Disparity Structure Based Graph Cut Algorithm With a Foreground Boundary Refinement Mechanism,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, pp. 784–801, 2013.

Y. S. Chen, Y. P. Hung, and C. S. Fuh, “Fast block matching algorithm based on the winner-update strategy,” IEEE Trans. Image Process., vol. 10.8, pp. 1212–1222, 2001.

L. Di Stefano, M. Marchionni, S. Mattoccia, G. Neri, and B. Sede, “A Fast Area-Based Stereo Matching Algorithm,” Image Vis. Comput., vol. 22.12, pp. 983–1005, 2004.

F. Tombari, L. Di Stefano, S. Mattoccia, A. Mainetti, and D. Arces, “A 3D Reconstruction System Based on Improved Spacetime Stereo,” Control Autom. Robot. Vis. (ICARCV), 2010 11th Int. Conf. on. IEEE, 2010.

M. Debella-gilo and A. Kääb, “Locally adaptive template sizes for matching repeat images of Earth surface mass movements,” ISPRS J. Photogramm. Remote Sens., vol. 69, pp. 10–28, 2012.

S. Lee, J. Sim, C. Kim, S. Member, and S. Lee, “Correspondence Matching of Multi-View Video Sequences Using Mutual Information Based Similarity Measure,” IEEE Trans. Multimed., vol. 15.8, pp. 1719–1731, 2013.

X. Mi, “Stereo Matching based on Global Edge Constraint and Variable Window Propagation,” Image Signal Process. (CISP), 2012 5th Int. Congr. on. IEEE, 2012.

S. J. Manap NA, “Disparity refinement based on depth image layers separation for stereo matching algorithms,” J. Telecommun. Electron. Comput. Eng., vol. 4(1), pp. 51–64, 2012.

J. Zhao and J. Katupitiya, “A multi-window stereo vision algorithm with improved performance at object borders,” Comput. Intell. Image Signal Process. 2007. CIISP 2007. IEEE Symp. on. IEEE, 2007.

A. A. Cigla, C., & Alatan, “Information permeability for stereo matching,” Signal Process. Image Commun., vol. 28.9, pp. 1072–1088, 2013.

K. Zhang, S. Member, J. Lu, and Q. Yang, “Real-Time and Accurate Stereo : A Scalable Approach with Bitwise Fast Voting on CUDA,” IEEE Trans. Circuits Syst. Video Technol., vol. 21.7, pp. 867–878, 2011.

D. G. and F. Girosi., “Parallel and deterministic al- gorithms from MRF’s: Surface reconstruction.,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 13.5, pp. 401–412, 1991.

L. L. Zhu, Y. Chen, Y. Lin, C. Lin, and A. Yuille, “Recursive Segmentation and Recognition Templates for 2D Parsing,” Adv. Neural Inf. Process. Syst., 2009.

I. Gerace, V. Vanvitelli, R. Pandolfi, and V. Vanvitelli, “A Color Image Restoration with Adjacent Parallel Lines Inhibition,” Image Anal. Process. 2003. Proceedings. 12th Int. Conf. on. IEEE, 2003.

R. Gouiaa and J. Meunier, “3D reconstruction by fusioning shadow and silhouette information,” Comput. Robot Vis. (CRV), 2014 Can. Conf. on. IEEE, 2014.

D. Neilson and Y. H. Yang, “A component-wise analysis of constructible match cost functions for global stereopsis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33.11, pp. 2147–2159, 2011.

F. Tombari, S. Mattoccia, L. Di Stefano, and E. Addimanda, “Classification and evaluation of cost aggregation methods for stereo correspondence,” Comput. Vis. Pattern Recognition, 2008. CVPR 2008. IEEE Conf. on. IEEE, 2008.

L. A. Aranda, P. Reviriego, and J. A. Maestro, “Error Detection Technique for a Median Filter,” IEEE Trans. Nucl. Sci., 2017.

R. Kunsoth, “Modified Decision Based Median Filter for Impulse Noise Removal,” Wirel. Commun. Signal Process. Netw. (WiSPNET), Int. Conf. on. IEEE, 2016.

B. Xiong and Z. Yin, “A Universal Denoising Framework With a New Impulse Detector and Nonlocal Means,” EEE Trans. Image Process., vol. 21, pp. 1663–1675, 2012.

C. Lin, J. Tsai, and C. Chiu, “Switching Bilateral Filter With a Texture / Noise Detector for Universal Noise Removal,” IEEE Trans. Image Process., vol. 19.9, pp. 2307–2320, 2010.

N. C. Gabbouj, M., Coyle, E. J., & Gallagher Jr, “An overview of median and stack filtering,” Circuits, Syst. Signal Process., vol. 11.1, pp. 7–45, 1992.

S. Arastehfar, A. A. Pouyan, and A. Jalalian, “An enhanced median filter for removing noise from MR images,” J. AI Data Min., vol. 1.1, pp. 13–17, 2013.

M. R. Rakesh, B. Ajeya, and A. R. Mohan, “Hybrid Median Filter for Impulse Noise Removal of an Image in Image Restoration,” Int. J. Adv. Res. Electr. Electron. Instrum. Eng., vol. 2.10, 2013.

Q. Yang, “A Hybrid Median Filter for Enhancing Dim Small Point Targets and Its Fast Implementation,” Multimed. Signal Process. (CMSP), 2011 Int. Conf. IEEE, vol. 1, pp. 239–242, 2011.

D. M. Bappy and I. Jeon, “Combination of hybrid median filter and total variation minimisation for medical X-ray image restoration,” IET Image Process., vol. 10.4, pp. 261–271, 2015.

Z. A. Mustafa, B. A. Abrahim, and Y. M. Kadah, “Modified Hybrid Median Filter for Image Denoising,” Radio Sci. Conf. (NRSC), 2012 29th Natl. IEEE, 2012.

A. B. Panchal, C. S., & Upadhyay, “Depth Estimation Analysis Using Sum of Absolute Difference Algorithm,” Int. J. Adv. Res. Electr. Electron. Instrum. Eng., vol. 3.1, 2015.

M. Khaparde, A., Naik, A., Deshpande, M., Khar, S., Pandhari, K., & Shewale, “Performance Analysis of Stereo Matching Using Segmentation Based Disparity Map,” ICDT 2013 Eighth Int. Conf. Digit. Telecommun., 2013.

### Refbacks

- There are currently no refbacks.

This work is licensed under a Creative Commons Attribution 3.0 License.

**ISSN: 2180****-1843**

**eISSN: 2289-8131**