Image optical flow optimization method and system based on residual field and displacement field

Abstract

The invention relates to an image optical flow optimization method and system based on a residual field and a displacement field. The method comprises the following steps: acquiring a reference image; estimating the reference image by adopting an optical flow estimation method to obtain an initial optical flow field; obtaining an optical flow optimization model, wherein the optical flow optimization model comprises an image encoder, an optical flow encoder, a first decoder and a sub-decoder; inputting the reference image and the initial optical flow field into the optical flow optimization model, and outputting a residual field and a displacement field; superposing the initial optical flow field and the residual field to obtain a preliminarily optimized optical flow field; and resampling the preliminarily optimized optical flow field by using the displacement field to obtain an optimized optical flow field. According to the invention, the precision and robustness of the image sequence optical flow estimation in the motion boundary region can be improved.

Description

technical field [0001] The invention relates to the field of image sequence optical flow calculation, in particular to an image optical flow optimization method and system based on a residual field and a displacement field. Background technique [0002] Optical flow is the two-dimensional instantaneous velocity of moving objects or pixel points on the surface of the scene on the projection plane. It can not only provide motion parameter information of moving objects and scenes in the image, but also provide rich three-dimensional structure information. It is an important tool in image processing, computer vision, etc. hot issues in the field. In recent years, with the rapid development of deep learning theory and technology, the convolutional neural network model has been widely used in the research of optical flow estimation technology. Because this type of method has significant advantages such as fast calculation speed and high stability, it has gradually become an import...

AI Technical Summary

Problems solved by technology

[0003] At present, the optical flow estimation technology based on the convolutional neural network is the most commonly used optical flow estimation technology in the image sequence optical flow calculation technology. The blurring of image sequences with rigid motion and large displacement is more serious, which limits the application of such methods in various fields

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