Paper Info
Title
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.
Year
DOI
Venue
2008
10.1117/12.766253
DIGITAL PHOTOGRAPHY IV
Keywords
Field
DocType
auto focusing, Depth from Focus, Depth from Defocus, discrete cosine transform, multilayer neural network
Computer vision,Shutter lag,Digital photography,Discrete cosine transform,Algorithm,Lens (optics),Artificial intelligence,Depth from defocus,Depth map,Classifier (linguistics),Discrete cosine transforms,Mathematics
Conference
Volume
ISSN
Citations 
6817
0277-786X
1
PageRank 
References 
Authors
0.39
0
5
Name
Order
Citations
PageRank
byung kwan park120.84
sungsu kim210.39
DaeSu Chung341.18
SeongDeok Lee42010.99
Chang-Yeong Kim515829.48