Joint Image Deblurring and Binarization for License Plate Images using Deep Generative Adversarial Networks

Image deblurring is a highly ill-posed inverse problem where it aims to estimate the sharp image from blurred image with or without the knowledge about the blurring process. Despite the success of model-based image deblurring methods where the deconvolution is a major step to recover the sharp image, its usage in practice is still limited, especially when many factors such as object motion, camera motion, non-uniform sensitivity of the imaging device contribute to imaging process. In automatic license plate recognition (ALPR) of moving vehicle, the blurred image severely reduces the accuracy of recognition. Meanwhile, though the binarized image of license plate has an important role in ALPR systems, its accuracy is largely affected by the blurred image. In this paper, we use a deep architecture based on Generative Adversarial Networks to jointly perform image deblurring and image binarization for license plate images. Our model directly maps from blurred image to binary image without going through the deblurring as in conventional method. The proposed method is benefited from the fact that the ground-truth, sharp license plates are difficult to acquire for moving object, while the accurate binary images can be manually derived from blurred ones.