Estimation of particle depth from two defocused images using the Fourier transform

Publication date: Available online 9 July 2019Source: ParticuologyAuthor(s): Wu Zhou, Xu Luo, Benting Chen, Yukun Zhang, Xiaoshu CaiAbstractDepth from defocus is one technology for depth estimation. We estimate particle depth information from two defocused images captured simultaneously by two coaxial cameras with different imaging distances. The images are processed with the Fourier transform to obtain the characteristic parameter (i.e., the standard deviation of the relative blur kernel of these two defocused images). First, we theoretically analyze the functional relationship between the object depth and the standard deviation or variation of the relative blur kernel. Then, we verify the relationship experimentally. We analyze the influence of particle size, window size and image noise on the calibration curves using both numerical simulations and experiments. We obtain the depth range and accuracy of this measurement system experimentally. For the verification experiments, we use a sample of glass microbeads and the irregularly-shaped dust particles on a microscope slide. Both of these experiments present a suitable depth measurement result. Finally, we apply the measuring system to the depth estimation of drops from a small anti-fogging spray. The results show that our system and image processing algorithm are robust for different types of particles, facilitating the in-line three-dimensional positioning of particles.Graphical abstract

Source: Particuology - July 10, 2019 Category: Science Source Type: research