Image processing method and device, electronic equipment and readable storage medium

Abstract

The embodiment of the invention provides an image processing method, an image processing device, electronic equipment, a readable storage medium and a computer program product. The method comprises the following steps: obtaining a first cumulative distribution law integrally corresponding to pixel values of all images in a to-be-processed image set; according to the first cumulative distribution law, obtaining corresponding cumulative distribution values of pixel values corresponding to pixel points of all the images in the first cumulative distribution law; and replacing the pixel values corresponding to the pixel points of all the images with the cumulative distribution values to obtain uniformly distributed transformation images corresponding to all the images. According to the image processing method provided by the embodiment of the invention, the frequency distribution integrally corresponding to the pixel values of all the uniformly distributed transformation images tends to be uniformly distributed, and the uniformly distributed transformation images corresponding to the images in the to-be-processed image set are used as sample data for model training; the convergence speed and the convergence effect of the neural network model can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to an image processing method. The present application also relates to an image processing device, an electronic device, a readable storage medium and a computer program product. Background technique [0002] In recent years, with the rapid development of computer technology, machine learning technology has also been continuously developed. Machine learning technology can be applied in various fields, such as: image recognition processing field, audio recognition processing field, etc. When applying machine learning technology to the field of image recognition, it is often necessary to train a neural network model with specific functions. In the process of training the neural network model, a large number of images are required as sample data. In order to obtain a large amount of sample data, data enhancement processing is often performed on images in the original...

AI Technical Summary

Problems solved by technology

However, only by obtaining new sample data to train the neural network model through the existing common methods of data enhancement for image sets, the convergence speed and convergence effect of the neural network model cannot be improved very well.

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