Image processing method and device, terminal equipment and storage medium
An image processing and image technology, applied in the field of image processing, can solve the problem of consuming large storage resources and computing resources, achieve the effect of reducing the amount of data calculation, improving the efficiency of convolution operations, and reducing the consumption of storage resources and computing resources
Pending Publication Date: 2021-12-10
SHENZHEN INTELLIFUSION TECHNOLOGIES CO LTD
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[0003] The embodiment of the present application provides an image processing method, device, terminal equipment and storage medium to solve the problem that the convolution operation of the image needs to consume a large amount of storage resources and computing resources in the prior art
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[0025] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
[0026] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude one or more other features. , whole, step, operation, element, component and / or the presence or addition of a collection thereof.
[0027] It shou...
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The invention provides an image processing method and device, terminal equipment and a storage medium. The image processing method comprises the steps of obtaining a to-be-processed image; inputting the to-be-processed image into the neural network to determine input data of any convolutional layer in the neural network; if the input data is represented by floating-point numbers, obtaining a scaling factor of the input data, and converting the input data from floating-point number representation to fixed-point number representation according to the scaling factor of the input data; obtaining a filter parameter represented by a fixed point number and an offset parameter represented by the fixed point number of the convolutional layer; and performing convolution operation on the input data represented by the fixed-point number, the filter parameter represented by the fixed-point number and the offset parameter represented by the fixed-point number to obtain output data of a convolution layer represented by the fixed-point number, the output data being a convolution operation result of the image to be processed. The problem that in the prior art, convolution operation on the image needs to consume a large number of storage resources and computing resources can be solved.
Description
technical field [0001] The present application belongs to the technical field of image processing, and in particular relates to an image processing method, device, terminal equipment and storage medium. Background technique [0002] With the great breakthrough of deep learning in the field of image processing, there has been an upsurge of deep learning research based on neural networks. The neural network is a floating-point model obtained through training, which consists of a series of operators and contains a large number of intensive operations. Convolution operation is a relatively common operator in neural network. When processing images through neural network, due to the large number of parameters and large amount of data in neural network, the convolution operation on images needs to consume a lot of storage resources and resources. computing resources. Contents of the invention [0003] Embodiments of the present application provide an image processing method, de...
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IPC IPC(8): G06N3/04G06N3/063G06F7/499
CPCG06N3/063G06F7/49915G06N3/045
Inventor 杨海辉蔡万伟尹长生
Owner SHENZHEN INTELLIFUSION TECHNOLOGIES CO LTD