A method of license plate image deblurring based on depth learning

Abstract

The invention provides a method of license plate image deblurring based on depth learning. The method is divided into a data set preprocessing stage, a training stage and a testing stage. In the dataset preprocessing stage, the license plate region in the image is determined, the license plate characters are segmented and the image size is normalized, Gaussian noise is added to obtain the training set. In the training phase, the linear sum of the mean square error, the gradient error and the discrimination error of the restoration result is used as the discriminator and generator of the network loss alternately. In the testing phase, the license plate characters are segmented and used as the input of the generator in turn, and the deblurring results are combined according to the originalorder of the license plate characters to obtain the deblurring license plate images. The model of the invention effectively restricts the edge of the license plate image, thereby improving the qualityof deblurring the license plate image and shortening the restoration time.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a method for deblurring a license plate image with motion blur, in particular to a method for deblurring a license plate image based on deep learning. Background technique [0002] Due to the influence of factors such as excessive vehicle speed, hardware limitations of the capture equipment, and lighting environment, the capture image has certain motion blur, which affects the accurate acquisition of the license plate number and has a negative impact on the management of urban traffic. License plate de-blurring refers to using the high-efficiency computing performance of the computer to restore the license plate image with motion blur through intelligent algorithms to obtain a clear license plate image. License plate recognition is an important part of traffic law enforcement, and the de-blurring of the license plate is beneficial to improve the recognition of the license ...

AI Technical Summary

Problems solved by technology

[0004] In recent years, scholars at home and abroad have conducted in-depth research and discussions on the estimation of motion blur kernels. Pan et al. proposed l 0 Regularization constraints estimate the image itself and its gradient in the middle, but when there are few zero-value pixels in the image, the sparse constraint of the image itself does not work, resulting in low restoration quality in license plate deblurring; Fang et al. proposed l 1 The regularization term constrains the sparsity of the image, which improves the quality of license plate deblurring, but there is some pixel distortion when restoring the image; Song et al. proposed to use the l 1 Sparse regularization constrains the image, and introduces a license plate recognition algorithm to optimize the deblurring effect, but the cyclic process of license plate deblurring and license plate recognition makes the entire license plate restoration process take too long

[0005] In recent years, with the rapid development of deep learning and the wide application in the field of computer vision, the problem of image de-blurring has been extensively studied. Svobode et al. proposed to use the convolutional neural network model to train the model of license plate image restoration. This method is limited by small The movement direction and length of the car within the range are not suitable for license plate de-blurring in complex situations; Nah et al. proposed to apply generative confrontation network to deal with motion blur in dynamic scenes, but the edge features of the restored image are not obvious, and motion still exists Vague

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