Depth extraction using adaptive blur channel selection for dual-aperture camera

Abstract

Depth extraction is an important area of research in computer vision as a promising technology for various applications using depth information. There are several methods for the depth extraction but in this paper, we introduce the Dual-aperture (DA) system which is a novel camera based on depth from defocus (DFD). However, conventional method for DA system has an object color dependency problem, which comes from color transition region between objects or textures in an object. Therefore, this problem is very important issue because it results in errors at depth map. For this reason, we propose consecutive process consisting of two parts to obtain the improved depth map compared to the conventional method. We demonstrate that the depth map becomes more even in an object at the same distance and distinguishable between objects at different distance by the proposed methods. In addition, experimental results indicate that we can obtain the improved depth map only with a small amount of additional computing time, or about 1% of the computing time for the conventional method.