In this paper, an iterative super-resolution reconstruction method is introduced for license plate recognition. A high-resolution image of the license plate is reconstructed by fusing the information derived from a set of subpixel shifted low-resolution images. The reconstruction problem is formulated as a system of linear equations that is solved by using the simultaneous algebraic reconstruction technique (SIRT). Simulation experiments show that SIRT can reconstruct a HR image with superior quality compared to conventional super-resolution reconstruction methods.
Abstract.A new superresolution algorithm is proposed to reconstruct a high-resolution license plate image from a set of low-resolution camera images. The reconstruction methodology is based on the discrete algebraic reconstruction technique (DART), a recently developed reconstruction method. While DART has already been successfully applied in tomographic imaging, it has not yet been transferred to the field of camera imaging. DART is introduced for camera imaging through a demonstration of how prior knowledge of the colors of the license plate can be directly exploited during the reconstruction of a high-resolution image from a set of low-resolution images. Simulation experiments show that DART can reconstruct images with superior quality compared to conventional reconstruction methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.