Fast and accurate auto focusing algorithm based on two defocused images using discrete cosine transform

Abstract

This paper describes the new method for fast auto focusing in image capturing devices. This is achieved by using two defocused images. At two prefixed lens positions, two defocused images are taken and defocused blur levels in each image are estimated using Discrete Cosine Transform (DCT). These DCT values can be classified into distance from the image capturing device to main object, so we can make distance vs. defocused blur level classifier. With this classifier, relation between two defocused blur levels can give the device the best focused lens step. In the case of ordinary auto focusing like Depth from Focus (DFF), it needs several defocused images and compares high frequency components in each image. Also known as hill-climbing method, the process requires about half number of images in all focus lens steps for focusing in general. Since this new method requires only two defocused images, it can save lots of time for focusing or reduce shutter lag time. Compared to existing Depth from Defocus (DFD) which uses two defocused images, this new algorithm is simple and accurate as DFF method. Because of this simplicity and accuracy, this method can also be applied to fast 3D depth map construction.