Image processing method and device and computer storage medium

Abstract

The invention discloses an image processing method and device and a storage medium. The method comprises the steps of obtaining a to-be-simulated 3D convolution model and training data; decomposing the 3D convolution model into cascading of a 3D space convolution model and a 3D time convolution model to obtain a pseudo 3D cascading convolution model; training a pseudo 3D cascade convolution modelby using the training data, and obtaining parameters of a 3D spatial convolution model and a 3D time convolution model; converting the 3D space convolution model and the 3D time convolution model intoa 2D space convolution model and a 2D time convolution model; setting a feature rearrangement rule for the 2D spatial convolution model and the 2D time convolution model; mapping model parameters ofthe 3D spatial convolution model and the 3D time convolution model into parameters of a 2D spatial convolution model and a 2D time convolution model to obtain a 2D cascaded convolution model; and performing convolution operation on the image by using the 2D spatial convolution model and the 2D time convolution model. By means of the mode, image processing conducted through 3D convolution operationcan be achieved through the 2D convolution model.

Description

technical field [0001] The present application relates to the technical field of video analysis, in particular to an image processing method, device and computer storage medium. Background technique [0002] Convolutional Neural Networks (CNN) is a type of Feedforward Neural Networks (Feedforward Neural Networks) that includes convolution calculations and has a deep structure, and is one of the representative algorithms for deep learning. Convolutional neural network has the ability of representation learning, and can perform shift-invariant classification on input information according to its hierarchical structure, so it is also called "Shift-Invariant Artificial Neural Networks". , SIANN)". [0003] Research on convolutional neural networks began in the 1980s and 1990s. Time-delay networks and LeNet-5 were the earliest convolutional neural networks; after the 21st century, with the introduction of deep learning theory and numerical calculation With the improvement of eq...

AI Technical Summary

Problems solved by technology

[0005] The technical problem mainly solved by this application is to provide an image processing method, which can solve the problem that the image processing technology realized by using the 3D convolution model in the prior art cannot be realized by the 2D convolution model

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