Using Wavelet Transformation and Edge Detection to Generate a Depth Map from a Single Image

Abstract

A depth map is essential for 3D display. Traditional depth maps are derived from multi-images. Producing a depth map from a single image is a more challenging problem and much more difficult than that of from multi-images. We propose a novel approach that combines wavelet transformation with edge detection to generate depth information from a single image. A depth map is then generated by depth prediction. Peak signal-to-noise ratio (PSNR), the structural similarity image measure (SSIM), and computation time are used to evaluate the performance of the proposed algorithm. The experimental results reveal that the PSNR and SSIM of the proposed algorithm are larger than those produced in the traditional approach. The computation time of the proposed algorithm is less than that produced by the traditional approach. Furthermore, 1000 test images and the crossponding depth maps are produced in this study. The average of the three metrics was found to be superior to those of the traditional approach. The statistic tests were also significant for PSNR and TIME between the two approaches. We have also written and files four patent applications with respect to this work.