Studies On Auto-focus System

Auto focusing is a key technique in digital image capture system and it is widely used in digital cameras, camcorders, medical imaging, video surveillance systems and so on. Whit the development of science, auto focusing is being paid more attention to than several years ago.Following is the process of auto focusing. Firstly, get images of different focusing level of the scene though image-capture instrument. Transforming the analog signal to digital signal, then send it to computer. Secondly, computer calculates the evaluation function values of these images and sequences to evaluate the definition of them. Finally, find the image with the biggest function value according to mountain climbing searching algorithm. Equipments take and save that image.Though these analyses, auto focusing mainly has three modules, including focusing region selection module, focusing evaluation function module and mountain climbing algorithm module. Focusing region selection module decides which pixels take part in evaluation function. Theoretically, all pixels taking part in evaluation will be most accurate. However, it is a waste of resources and time that all pixels take part in calculation. Only those important ones need to be considered. That is the main work of region selection module. Focusing evaluation function aims at evaluating the definition of images, evaluating the level of focusing, or defocusing in another word, thus to supply a foundation for controlling the lens. So it is ideal that focusing position’s function value obviously distinguishes from other positions’values. Mountain climbing algorithm finds the image with the biggest evaluation function value fleetly and accurately.In the region selection module, a variety of window selecting methods have been proposed in the literature such as central window and gauss un-uniformed sampling. To any image, especially the main target offset from the center of the scene, the traditional focus windows contain a large amount of background information led to the evaluation function has many extremes and focus on failure, on other hand, the great amount of computation increases the focus time. For the traditional region selection algorithm exists some limitations, a method base on swarm intelligence optimization algorithm is proposed. Foreground image can be segmented fast and accurately using the method of quantum particle swarm optimization, focus accuracy of foreground and pixels used in calculating are decreased through the selection of edge region. Experiment results show that this method is self-adapting and can track off-center target.One key of auto-focus is the ability of focusing evaluation function decides the definition of image. And the mountain climbing algorithm influences the real-time and accuracy. In order to increase the real-time and accuracy, a new method of auto focusing is proposed. The algorithm, which combines improved gray scale difference method and high-accuracy wavelet function method, is able to properly select the focusing evaluation function and step length according to the gradients of the focusing evaluation function in each period of the focusing process. It realized the transition from fast rough adjustment to accurate one. The results of experiment show that compared with the traditional algorithm, the algorithm proposed shortens the focusing time while ensuring the accuracy of focusing.To sum up, the method of region selection base on quantum particle swarm optimization and the new method of auto focusing which combines the evaluation function and mountain climbing algorithm can improve the performance of auto focusing. They can achieve favorable effect.