Dynamic mask generation based on peak to correlation energy ratio for light reflection and shadow in PIV images

• Published in: Measurement : journal of the International Measurement Confederation Vol. 229; p. 114352
• Main Authors: Lemos, Bernardo Luiz Harry Diniz, de Lima Amaral, Rodrigo, Bortolin, Vítor Augusto Andreghetto, Lemos, Marcelo Luiz Harry Diniz, de Moura, Helder Lima, de Castro, Marcelo Souza, José de Castilho, Guilherme, Meneghini, Julio Romano
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2024
• Subjects: Dynamic mask; Interrogation window strategy; Particle image velocimetry; Peak to correlation energy; Particle image velocimetry; Peak to correlation energy; Interrogation window strategy; Dynamic mask
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2024.114352

[Display omitted] •A new approach for dynamic mask generation from PIV measurements is proposed.•Peak to Correlation Energy (PCE) is used as the basis of the new approach.•Signal and noise are represented by high and low PCE values, respectively.•PCE values are organized and a threshold is automatically estimated.•The efficacy of this approach is demonstrated through synthetic and experimental PIV. The performance of particle image velocimetry (PIV) measurements directly depends on the identification and removal of noisy regions in the field of view. Regions in the image with shadows and light reflections directly affect the PIV correlation and consequently deteriorate the vector field. This work proposes a new approach for mask generation based on peak correlation energy (PCE) ratio to remove the noisy effects of light reflection and shadow in PIV images. This approach considers that it is possible to relate low and high PCE values to noise and signal, respectively. Based on this, the PCE values are organized in a probability density function and a threshold is automatically estimated to separate the two regions. Furthermore, a mask correction step is proposed in the present method. The efficacy of this method is demonstrated through cases using both synthetic and experimental images, showcasing its potential to improve PIV analysis accuracy.