Throughout the years, digital particle imaging velocimetry (PIV) has developed into a technique that can be used in more and more complicated systems (Adrian 2005). It has been successfully applied in turbulent flows, two-phase flows, and flows around objects. The performance of the technique is determined by the quality of the images and by the signal treatment after acquisition (Raffel et al. 2007). If the quality of the original images is good, i.e., even illumination, good contrast, low background noise, few stationary objects, suitable tracer particle displacement etc., image processing will be relatively straightforward. In practice, however, these conditions cannot always be accomplished. Laser intensity can vary between images or image pairs due to differences in the two lasers (in case of a double-pulsed YAG laser), objects or bubbles can introduce strong reflections of light, and/or reflection from channel walls in confined flows introduce glow. Several approaches are available in literature to tackle these problems (Seol and Socolofsky 2008; Lindken and Merzkirch 2002; Honkanen and Nobach 2005; Westerweel 1993; Shavit et al. 2007; Theunissen et al. 2008). In the presence of static objects or in two-phase flow, fluorescent particles and a color filter are often used to avoid interference of the object edges in the correlation map. In the case of two-phase flow, two cameras (of which one has a color filter) may be used to optically separate the two phases before correlation. Although this enables PIV of two-phase flow and edge flow, the average intensity of the tracer particles is lower (Raffel et al. 2007). In some cases, this will render the correlation rather difficult. Furthermore, fluorescent particles are about a factor ten more expensive than non-fluorescent particles, and calibration of the two cameras is more complicated (Seol and Socolofsky 2008).
Applying a static mask to block stationary objects from an image is a well-known technique to prevent these objects from interfering with the correlation map. However, if an object is semi-transparent, information on flow behind the object will be lost. To cope with moving bubbles shadowgraphy is often used (Lindken and Merzkirch 2002). This approach uses a second camera and background lighting to capture the shadows of moving objects, which can then be used to mask these areas from the images from the first camera. However, this approach requires an additional camera and careful alignment of the images from the two cameras. Since a large difference in size and intensity between the object and tracer particles is often present, these objects can also be identified and masked using only the original images from a single camera.
The use of an additional camera can be circumvented by removing the stationary objects through a background subtraction, which will leave moving tracer particles in the image as suggested by Honkanen and Nobach (2005). Image normalization to cope with uneven illumination was already suggested by Westerweel (1993). This methodology scales the intensity in the original image to a suitable minimum and/or maximum value. Common examples are subtracting a sliding minimum, sliding average, or scaling to the sliding minimum and -maximum. The last option is very attractive in applications with low light intensity, since the technique enhances tracer particle visibility. Since this approach also reduces the relative intensity of bright objects (bubble or object reflections) compared to the particle intensities, the relative contribution of these objects in the correlation function is also reduced (Shavit et al. 2007). Combining image normalization with background subtraction solves the problems of temporal and spatial variation in the intensity distribution, as discussed by Theunissen et al. (2008) recently.
In this work, we introduce a combined approach that can be implemented at relative ease, which tackles uneven illumination, the presence of stationary objects and moving objects (i.e. bubbles) without the use of additional hardware. This approach consists of intensity normalization (to cope with uneven illumination), followed by background subtraction (to remove stationary objects) and image masking (to remove the bubbles in two-phase flow). We demonstrate the capabilities of this approach with two examples: single-phase flow in spacer-filled channels and an extension to two-phase flow in these channels.