Image processing method and device, electronic equipment and storage medium

Abstract

The invention relates to an image processing method and device, equipment and a storage medium. The method comprises the following steps: obtaining an original batch of images to be processed; according to the image processing configuration information of the convolution kernel of the neural network, obtaining a target segmentation number and a target convolution algorithm suitable for the original batch of images; equally segmenting the original batch of images according to the target segmentation number; obtaining multiple sub-batch images, and sequentially inputting the plurality of sub-batch images into a convolution kernel, so that the convolution kernel sequentially performs convolution operation on the plurality of sub-batch images by using a target convolution algorithm to obtain aplurality of sub-image processing results corresponding to the plurality of sub-batch images, and sequentially splicing the plurality of sub-image processing results to obtain an image processing result corresponding to the original batch image. According to the method and the apparatus, the sub-batch images can be subjected to rapid convolution operation in sequence by repeatedly utilizing a relatively small memory space, and the sub-batch images are spliced after operation to obtain a result equivalent to the original batch image, so that the effect of ensuring the image processing efficiency in the relatively small memory space is achieved.

Description

technical field [0001] The present disclosure relates to the technical field of image processing, and in particular, to an image processing method, device, electronic equipment, and storage medium. Background technique [0002] With the rapid development of information processing technology, artificial intelligence technology has been widely used in electronic devices such as mobile phones and computers, and the importance of deep learning technology in various business scenarios has become increasingly prominent. As the structure of the deep learning model becomes more and more complex and the application scenarios become more and more abundant, increasing the image processing speed of the neural network model is receiving more and more attention. [0003] The image processing method provided by traditional technology requires each convolution kernel to traverse various feasible convolution operation methods in the construction stage of the neural network to perform convolu...

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Problems solved by technology

[0004] The present disclosure provides an image processing method, device, electronic equipment, and storage medium to at least solve the technical problem that it is difficult to ensure image processing efficiency under the condition of limited memory space in the traditional technology

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