Neural network training and image processing method, device, electronic device and storage medium

Abstract

Embodiments of the present invention provide a neural network training method, device, electronic device and storage medium, and an image processing method, device, electronic device and storage medium. Among them, the neural network training method includes: obtaining a noisy sample image and a corresponding noise-free sample image; based on the neural network, generating a noise-free estimated image corresponding to the noisy sample image; according to the noise-free estimated image and the noise-free sample image, The neural network is trained; wherein, the neural network is a bilinear neural network. By adopting the technical scheme of the present invention, the problem of image noise overfitting that is prone to occur during neural network training can be avoided, thereby ensuring the influence of the trained neural network on the information in the image, and training through the neural network training method of this embodiment The obtained neural network can realize the defog and denoise processing of the natural image with fog and noise, and effectively improve the defog and denoise effect.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of artificial intelligence, and in particular to a neural network training and image processing method, device, electronic equipment, and storage medium. Background technique [0002] With the development of computer technology and image processing technology, image recognition is widely used in many fields, such as video surveillance, face recognition and so on. Image recognition can identify various target objects by processing, analyzing and understanding images. When performing image recognition, the higher the clarity of the image, the higher the accuracy of recognition. [0003] However, the captured image will be affected by the environment and the air, especially in the case of fog, haze, rain and other bad weather conditions, it is impossible to capture a clear image that is easy for subsequent identification. For this reason, the dehazing technology of natural images cam...

AI Technical Summary

Problems solved by technology

This method does not consider the cause of image degradation, and has a wide range of applications. It can effectively improve the contrast of foggy images and improve the visual effect of images, but it will cause a certain loss of information on prominent parts.

Dehazing algorithms based on image restoration, such as dehazing algorithms based on prior information, include: dark channel dehazing algorithm, algorithm that assumes that object shadows and reflectance are locally uncorrelated, etc., these algorithms can get better dehazing effects , but need to use prior information to estimate, and enhance the noise in the original image and other unnatural information in the process of defogging

[0005] It can be seen that the existing natural image defogging methods will have a considerable impact on the information in the original image, and cannot achieve the natural image defogging process well.

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