Image depth extraction method

Abstract

The invention discloses an image depth extraction method, which comprises the following steps of: (1) performing Gaussian fuzzy processing on an original image to be processed to obtain N fuzzy images, wherein N is more than or equal to 2; (2) detecting the edges of the original image and the N fuzzy images to obtain an edge image of each image; (3) calculating the corresponding fuzzy parameter estimation value of each pixel point under each Gaussian filtering parameter on the edges based on the edge images; (4) analyzing N fuzzy parameter estimation values of each pixel point on the edges by using a statistical method to obtain an optimal fuzzy parameter estimation value of each pixel point on the edges; (5) calculating the depth value of each pixel point on the edges in the images according to the optimal fuzzy parameter estimation value of each pixel point on the edges to obtain a sparse depth map; and (6) performing interpolation processing on the sparse depth map to obtain a dense depth map. Compared with a method in the prior art, the method has the advantages that high-accuracy fuzzy parameters can be obtained, so that a depth value obtained through subsequent calculation has high accuracy.

Description

【Technical field】 [0001] The invention relates to the field of computer vision, in particular to an image depth extraction method. 【Background technique】 [0002] The depth extraction method is used to obtain the depth information of each pixel in the image to be processed, and obtain the global depth map of the image to be processed, which plays an important role in the application fields of computer vision and computer graphics. [0003] Existing methods for extracting a depth map based on a single image are mainly divided into three categories, one of which is to obtain the blur parameter value of the image through a Gaussian re-blurring method. The sparse depth map is obtained by performing Gaussian re-blurring on the original image and solving the blur parameters after edge detection. Then perform depth growth on the obtained sparse depth map, and finally refine the depth image with bilateral filtering to obtain the final dense depth map. However, in the above method,...

AI Technical Summary

Problems solved by technology

However, in the above method, since the edge information is obtained by one-time calculation of the gradient ratio to obtain the blur information, the confidence of the blur parameter value of the edge image is not high

In addition, the edge of different regions is quite different, and it is easy to ignore the local feature information through one-time global calculation, which greatly reduces the accuracy of the edge value.

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