Edge Detection Operators: Peak Signal to Noise Ratio Based Comparison

Full Text (PDF, 1245KB), PP.55-61

Views: 0 Downloads: 0


D. Poobathy 1,* R. Manicka Chezian 1

1. Dr. Mahalingam Centre for Research and Development, NGM College, Pollachi, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2014.10.07

Received: 15 May 2014 / Revised: 26 Jun. 2014 / Accepted: 7 Aug. 2014 / Published: 8 Sep. 2014

Index Terms

Canny operator, Edge Detectors, Laplacian of Gaussian, MSE, PSNR, Sobel operator


Edge detection is the vital task in digital image processing. It makes the image segmentation and pattern recognition more comfort. It also helps for object detection. There are many edge detectors available for pre-processing in computer vision. But, Canny, Sobel, Laplacian of Gaussian (LoG), Robert’s and Prewitt are most applied algorithms. This paper compares each of these operators by the manner of checking Peak signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) of resultant image. It evaluates the performance of each algorithm with Matlab and Java. The set of four universally standardized test images are used for the experimentation. The PSNR and MSE results are numeric values, based on that, performance of algorithms identified. The time required for each algorithm to detect edges is also documented. After the Experimentation, Canny operator found as the best among others in edge detection accuracy.

Cite This Paper

D. Poobathy, R. Manicka Chezian,"Edge Detection Operators: Peak Signal to Noise Ratio Based Comparison", IJIGSP, vol.6, no.10, pp.55-61, 2014. DOI: 10.5815/ijigsp.2014.10.07


[1]Poobathy D and R. Manicka Chezian, “Recognizing and Mining Various Objects from Digital Images”, Proceedings of 2nd International Conference on Computer Applications and Information Technology (CAIT 2013), pp. 89-93, 2013. 

[2]G.Padmavathi, P.Subashini, and P.K.Lavanya, “Performance evaluation of the various edge detectors and filters for the noisy IR images”, Sensors, Signals, Visualization, Imaging, Simulation and Materials, pp. 199 – 203. 

[3]Jie Yanga, Ran Yanga, Shigao Lib, S.Shoujing Yina, and Qianqing Qina, “A Novel Edge Detection Based Segmentation Algorithm for Polarimetric Sar Images”, The International Archives of the Photogrammetry, Remote sensing and Spatial Information, Sciences. Vol. XXXVII, Part B7.Beijing, 2008. 

[4]Pinaki Pratim Acharjya, Ritaban Das and Dibyendu Ghoshal, “Study and Comparison of Different Edge Detectors for Image Segmentation”, Global Journal of Computer Science and Technology Graphics & Vision, Volume 12 Issue 13, pp. 28-32, 2012. 

[5]Pushpajit A. Khaire and Dr. N. V. Thakur, “A Fuzzy Set Approach for Edge Detection”, International Journal of Image Processing (IJIP), Volume 6 Issue 6, pp. 403-412, 2012. 

[6]Gonzalez, Rafael C., Richard E. Woods, and S. L. Eddins, "Image segmentation." Digital Image Processing, pp. 700-703, 2011. 

[7]Othman, Zolqernine, Habibollah Haron, Mohammed Rafiq Abdul Kadir, and Mohammed Rafiq. "Comparison of Canny and Sobel Edge Detection in MRI Images.", pp. 133-136, 2009. 

[8]Raman Maini and Dr. Himanshu Aggarwal, “Study and Comparison of Various Image Edge Detection Techniques”, International Journal of Image Processing (IJIP), Volume (3): Issue (1), pp. 1 – 12, 2000. 

[9]G.T. Shrivakshan and Dr.C. Chandrasekar, “A Comparison of various Edge Detection Techniques used in Image Processing”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, No 1, pp. 269 – 276, 2012. 

[10]P. Vidya, S. Veni and K.A. Narayanankutty, “Performance Analysis of Edge Detection Methods on Hexagonal Sampling Grid”, International Journal of Electronic Engineering Research, Volume 1, pp. 313–328, Number 4 2009. 

[11]Peter Kellman and Elliot R. McVeigh, “Image Reconstruction in SNR Units: A General Method for SNR Measurement”, Magn Reson Med. Author manuscript, Magn Reson Med. Volume 54(6) pp. 1439–1447, December 2005.