The invention discloses an image depth estimation algorithm based on deep learning and Fourier domain analysis. The image depth estimation algorithm comprises the steps: reading an image pair in a data set, and inputting the image pair into a proposed network architecture to obtain a reconstruction view; substituting the image pair and the reconstructed view into a loss function, and training andconfirming a network architecture to obtain an initial disparity map; substituting the image pair clipped according to the clipping ratio lambda into the confirmed network architecture to obtain a parallax mapping candidate object; and fusing the parallax mapping candidate objects, and estimating the depth and displaying the depth on a display device, wherein the image pair area is divided into aleft view and a right view, and the reconstructed view area is divided into a reconstructed left view and a reconstructed right view. According to the image depth estimation algorithm, based on the network architecture of deep learning and the Fourier domain analysis unsupervised monocular image estimation deep learning algorithm, the precision of the depth map and the precision of moving image depth estimation are effectively improved, and the accuracy and robustness of the algorithm are improved through the improved network architecture.
Image depth estimation algorithm based on deep learning and Fourier domain analysis