Author(s)

Ahmad Zaid Nazari¹, Yojiro Ishino^1*, Fumiya Ito¹, Harumi Kondo¹, Ryoya Yamada¹, Takanori Motohiro¹, Yu Saiki¹, Yoshiaki Miyazato², Shinichiro Nakao²

¹Electrical and Mechanical Engineering Department, Nagoya Institute of Technology, Nagoya, Japan.
²Department of Mechanical Systems Engineering, The University of Kitakyushu, Kitakyushu, Japan.

This report deals with introducing two new techniques based on a novel concept of complex brightness gradient in quantitative schlieren images, “inverse process” and “multi-path integration” for image-noise reduction. Noise in schlieren images affects the projections (density thickness) images of computerized tomography (CT). One spot noise in the schlieren image appears in a line shape in the density thickness image. Noise effect like an infectious disease spreads from a noisy pixel to the next pixel in the direction of single-path integration. On the one hand, the noise in the schlieren image reduces the quality of the image and quantitative analysis and is undesirable; on the other it is unavoidable. Therefore, the importance of proper noise reduction techniques seems essential and tangible. In the present report, a novel technique “multi-path integration” is proposed for noise reduction in projections images of CT. Multi-path integration is required the schlieren brightness gradient in two orthogonal directions. The 20-directional quantitative schlieren optical system presents only images of schlieren brightness in the horizontal gradient and another 20-directional optical system seems necessary to obtain vertical schlieren brightness gradient, simultaneously. Using the “inverse process”, a new technique enables us to obtain vertical schlieren brightness gradient from horizontal experimental data without the necessity of a new optical system and can be used for obtaining any optional directions of schlieren brightness gradient.

Noise Reduction Technique, Inverse Process, Quantitative Schlieren Optical System, Complex Schlieren Brightness, Computerized Tomography (CT)

Nazari, A. Z. , Ishino, Y. , Ito, F. , Kondo, H. , Yamada, R. , Motohiro, T. , Saiki, Y. , Miyazato, Y. and Nakao, S. (2020) Quantitative Schlieren Image-Noise Reduction Using Inverse Process and Multi-Path Integration. Journal of Flow Control, Measurement & Visualization, 8, 25-44. doi: 10.4236/jfcmv.2020.82002.