A new stereo matching method for RAW image data based on improved SGBM
Yan Liu, Wei Huang, Yuheng Wang · 2018
Traditionally, stereo vision algorithms are performed after color image processing pipeline, which includes demosaicing, color correction, white balance, etc. Color image processing pipeline possibly causes a loss of key information and introduces artifacts into final output image, which may lead stereo matching accuracy decreased. Hence, we implemented stereo matching algorithm on RAW data before color image processing pipeline to improve binocular stereo vision system matching accuracy. We proposed using RAW data in stereo matching enhance the robustness and accuracy of binocular stereo vision algorithm. Our approach focus on the first stage of many stereo algorithms: stereo matching. We approach the problem by using improved SGBM (Semi-Global Block Matching) algorithm. The proposed algorithm is tested on RAW image pairs captured by stereo camera system, and the experiments indicates that the algorithm is effective.