TITLE:
Quantitative Schlieren Image-Noise Reduction Using Inverse Process and Multi-Path Integration
AUTHORS:
Ahmad Zaid Nazari, Yojiro Ishino, Fumiya Ito, Harumi Kondo, Ryoya Yamada, Takanori Motohiro, Yu Saiki, Yoshiaki Miyazato, Shinichiro Nakao
KEYWORDS:
Noise Reduction Technique, Inverse Process, Quantitative Schlieren Optical System, Complex Schlieren Brightness, Computerized Tomography (CT)
JOURNAL NAME:
Journal of Flow Control, Measurement & Visualization,
Vol.8 No.2,
April
30,
2020
ABSTRACT: 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.