Estimation of Roughness Parameters of A Surface Using Different Image Enhancement Techniques (TECHNICAL NOTE)


1 ECE Department, Anil Neerukonda Institute of Technology& Sciences, Visakhapatnam, India

2 ECE Department, GITAM University, Visakhapatnam, India


Surface roughness measurement is widely used to estimate the quality of the product during manufacturing processes. It has a great importance in manufacturing fields such as ceramic tiles, glass, and iron. Many are using surface profile-meter with a contact stylus to measure the surface roughness of work piece. In the stylus method, a stylus is moved along the surface and the vertical movement of the stylus is recorded to measure surface roughness. This method has the disadvantage that work piece surface may damage due to direct contact between the surface and the stylus. In this paper, we propose a novel technique to find the roughness parameters of a surface by using image processing techniques like Contrast stretching and Bi-cubic interpolation techniques of image enhancement. In these techniques, firstly the surface image of the work pieces is acquired using the digital camera and it is pre-processed in order to remove noise and then image enhancement is done followed by parameters analysis. The roughness parameters such as average surface roughness (Ra), Maximum valley profile depth (Rv (Valley)), Highest peak (Rp (Peak)), Root-mean-square (rms) roughness (Rq (rms)) were determined using above techniques. The results obtained by the both methods are tabulated and compared.


1.     Young, P. L., Brackbill, T. P. and Kandlikar, S. G., "Comparison of roughness parameters for various microchannel surfaces in single-phase flow applications", Heat Transfer Engineering,  Vol. 30, No. 1-2, (2009), 78-90.

2.     Ondra, J., "Measurement of roughness using image processing", Department of Mechanical Technology Military Academy Brno, 612 00 Brno, Czech Republic.

3.     Narayanan, M. R., Gowri, S. and Krishna, M. M., "On line surface roughness measurement using image processing and machine vision", Proceedings of the World Congress on Engineering  Vol. I, (2007).

4.     Gonzalez, R. and Wintz, P., "Digital image processing", United States: Addison-Wesley Publishing Co., Inc., Reading, MA., (1977).

5.     Persson, U., "Surface roughness measurement on machined surfaces using angular speckle correlation", Journal of Materials Processing Technology,  Vol. 180, No. 1, (2006), 233-238.

6.     Sivasankar, S., Jeyapaul, R., Kolappan, S. and Shaadil, N., "Procedural study for roughness, roundness and waviness measurement of edm drilled holes using image processing technology", Computer Modelling and New Technologies, Vol.16, No.1, (2012), 49–63.

7.     Tang, X., Xiao, H., Ding, H. and Liu, J., "Surface roughness measurement based on image processing and image recognition", Computers and Simulation in Modern Science, (2009), 91-96.

8.     Maradudin, A. A., "Light scattering and nanoscale surface roughness, Springer Science & Business Media,  (2010).

9.     Shrestha, S., "Image denoising using new adaptive based median filters",An International Journal (SIPIJ),  Vol. 5, No. 4, (2014), .

10.   Juneja, M. and Mohana, R., "An improved adaptive median filtering method for impulse noise detection", International Journal of Recent Trends in Engineering,  Vol. 1, No. 1, (2009), 274-278.

11.   Ambale, V., Ghute, M., Kanchan, K. and Ktre, S., "Adaptive median filter for image enhancement", International Journal of Engineering Science and Innovative Technology (IJESIT),  Vol. 2, No. 1, (2013), 318-323.

12.   Mehta, R. and Aggarwal, N. K., "Comparative analysis of median filter and adaptive filter for impulse noise–a review", International Journal of Computer Applications (0975 – 8887), National Conference on Recent advances in Wireless Communication and Artificial Intelligence (RAWCAI-2014).

13.   Remimol, A. and Sekar, K., "A method of DWT with bicubic interpolation for image scaling", International Journal of Computer Science Engineering (IJCSE),  Vol. 3, No. 02, (2014), 131-135.

14.   Jing, L., Zongliang, G. and Xiuchang, Z., "Directional bicubic interpolation-a new method of image super-resolution", Proceedings of ICMT, Atlantis Press,  (2013), 470-477.