Spatial correction and Deblurring fusion algorithm for vehicle license plate images based on deep learning

Image correction and deblurring have always posed significant challenges in the field of image processing, and the results of license plate image recognition with low structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) will significantly affect the use of vehicles. To address these problems, we propose a real-time algorithm based on deep learning, which can simultaneously perform image spatial correction and deblurring in the same network, known as Formula: see text. First, a 15-layer depthwise separable convolutional neural network is designed as the basic network for high-speed image deblurring, and four spatial transformer network (STN) modules are integrated into the basic network to achieve image spatial correction capability. Second, the network is divided into three dense blocks, in which dense residual connections are used to improve the PSNR. Finally, a bottleneck structure is created to improve the real-time performance of image reconstruction that can simultaneously perform image space correction and deblurring. The test results on the CCPD dataset demonstrate that the Formula: see text algorithm is advanced, with PSNR reaching 30.14Formula: see textdB, SSIM reaching 0.911, and image reconstruction only 0.12Formula: see texts. The correction and deblurring modules can promote each other, jointly improving the image reconstruction ability of the network, that is, the network achieves more advanced image reconstruction performance while maintaining high real-time performance.