Image rain removal method based on physical modeling generative adversarial learning

Abstract

The invention belongs to the field of image processing and computer vision, and provides an image rain removal method based on physical modeling generative adversarial learning, which realizes generative adversarial learning through a double-layer normal form model. The generative adversarial network is mainly composed of two parts: a generator and a discriminator, the generator generates a target result by using input random noise, and the discriminator discriminates the result generated by the generator until the discriminator considers that the result of the generator meets the target requirement. A traditional generative adversarial network is completely based on data driving, and the functions of a generator and a discriminator are achieved through a convolutional neural network. However, due to particularity of a rain removal task, a large amount of data is difficult to obtain, so that generative adversarial learning is realized from the perspective of model optimization, modeling is performed on a generator and a discriminator through an upper layer model and a lower layer model based on a double-layer normal form means, and an optimal rain removal result is obtained through the adversarial learning of the upper layer model and the lower layer model.

Description

technical field [0001] The invention belongs to the field of image processing and computer vision, and relates to an image deraining method based on physical modeling generation confrontation learning. Background technique [0002] With the development of computer hardware equipment, the processing speed of image and video data on PC and mobile terminals has been greatly improved. Life has brought many conveniences, such as: facial recognition technology when clocking in at work, reversing images when driving, traffic safety monitoring and monitoring systems, and so on. Along with it, many technologies related to image equipment and video processing are also constantly developing and improving, which in turn pushes the application range of the entire image field to become wider and wider. However, one limitation that cannot be ignored in the field of vision is the impact of severe weather. For example, in rainy weather, not only people's visual recognition of objects will ...

AI Technical Summary

Problems solved by technology

CN108765327A uses discriminative sparse coding to describe the rain line, but this will also cause color distortion to a certain extent in the output picture

CN111815526A provides a method for removing rain stripes in rainy images based on image filtering and CNN, which solves the problems that the existing rain removal methods are difficult to accurately describe the rain model, the rain stripes cannot be removed cleanly, and details are easily lost

Although the algorithm based on deep learning solves the problem of rain removal to a certain extent, it requires a large number of rain-no-rain data pairs for training to ensure the robustness of the algorithm

However, it is very difficult to simultaneously collect rain-no-rain data pairs under natural conditions, and the types of existing data are relatively single. Therefore, the current image deraining method based on deep learning is very limited in practical application. The effect is obvious for scenes similar to the characteristics of the training data, but it cannot be solved well for real scenes that are quite different from the characteristics of the training data.

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