Research Of Digital Speckle Correlation Techniques

Owing to simple system and low environmental requirement, digital speckle correlation technique has been widely used in various measurement fields as an important optical measurement technique. The simple operation, non-contact with specimen surface, and the ability of full field measurement constitute the advantages of the technique for high accuracy method in small deformation and displacement measurement.The basic principle of digital speckle correlation technology is described in this paper. Also several search algorithms and two acquisition methods of the sub-pixel displacement are introduced. The sub-pixel displacement information which calculated by most high accuracy algorithms of digital speckle correlation need to get the pixel information first. The particle swarm algorithm is an intelligent search method from simulation of biological population which has a strong capacity of global and local search abilities.By using the method of gray-level interpolation, sub-pixel sub-areas are constructed to improve the digital speckle correlation method which is based on the particle swarm optimization algorithm. The integer pixel displacement information and the sub-pixel displacement information can be calculated at the same time. The main contents and innovations are summarized as follows:1. Through the numerical simulation of the constructed speckle pattern, the effects of different parameters which affect the convergernce of the particle swarm optimization algotithm and the deviations of different magnitude measurement are discussed in this paper. The reliability of the algorithm in sub-pixel displacement measurement is verified by the results.2. The in-plane translation experiment, rotation experiment and general movement experiment are made by the specimen of speckle. The particle swarm optimization speckle correlation algorithm is compared to the curved-surface fitting speckle correlation algorithm on the measuring accuracy and the different magnitude measurement. The experimental results show that the particle swarm speckle correlation algorithm is superior to the curved-surface fitting speckle correlation algotithm. At the same time the particle swarm speckle correlation algorithm has higher orders of magnitude in the measurement.3. The sub-pixel particle swarm optimization algorithm of different orders of magnitude interpolation is compared to the integer pixel particle swarm algorithm in this paper. The comparison confirmed that the particle swarm algorithm based on sub-pixel speckle correlation method could achieve good effect of different orders of magnitude especially for the small displacements.