Studies On Measure Function Performance And Dynamic Region Selection In Auto-focus System

Autofocus (AF) is a key technique in digital image capture system and it developed rapidly in recent years. Now the autofocus technique is widely used in many visual applications such as digital cameras, camcorders, video surveillance systems and microscopes. The AF quality directly affects the imaging quality and efficiency, and therefore it attracts us to explore. The AF algorithm based on image processing is usually divided into two classes:depth from defocus (DFD) and depth from focus (DFF). DFD extracts focal depth information from defocus images, and it realizes focus fast but has low precision while DFF is opposite.Because of high precision and flexibly, passive DFF method is used in most autofocus system. An AF system consists of three main modules:a focusing window which defines the region to be focused, a focus measure function (FMF) that evaluates the image sharpness, and a searching strategy to find the global maximum of the FMF. Currently, most imaging systems adopt this semi-digital AF technique, which has the analysis module to determine the focusing status by computing the sharpness of the input image, and the control module to move the lens back and forth until the best focused image is obtained.Imaging lens can be equivalent to a low-pass filter, so the quality of image depends on high coefficients. The FMF usually selects the high coefficients as focus measurement. In order to improve the sharpness and anti-noise ability, this paper proposed a new evaluation function named 2-D weighted DCT. Compared to the common evaluation functions such as gray variance and gradient function,2-D weighted DCT and discrete wavelet transform (DWT) have the best performance. The experiment shows that 2-D weighted DCT performs better in real-time than DWT.Generally, our interest area is the foreground image, so the focus region selection algorithm should adopt the prospects for focus window to reduce data quantity. A variety of window selecting methods have been proposed in the literature such as central window and gauss un-uniformed sampling. For the traditional region-selection algorithm exists some limitations, a method based on intelligence optimization algorithm is proposed; for the standard particle swarm optimization (PSO) and artificial fish swarm algorithm(AFSA) are easy lost in local optimum, the parameters' setting and action are improved in this paper. Experiment results show that foreground image can be segmented fast and accurately using this method. Focus accuracy of foreground and pixels used in calculating are decreased through the selection of edge region, so the real-time of AF is enhanced; the proposed method can track the main object in image, so the region got in this method is dynamic with good adaptability.At present, adoption of focus scheme is usually single DFF or DFD, and many literatures optimize part module algorithm is based on this study. Thus, this paper puts forward a new focus strategy that combines the high precision of DFF and speed advantage of DFD. Deduction is given in theory and a preliminary experiment also proved its feasibility.Anyhow, the mature imaging system and the latest auto-focusing technologies are controlled by Japan and USA, our country is still not mature in theoretical level and technical level, so thorough research on AF has both theory value and application value.