A learning-based single-image super-resolution method for very low quality license plate images
Alexandre Nata Vicente, Hélio Pedrini · 2016
Spatial resolution enhancement of license plate images in real scenarios plays an important role in the fields of criminal investigation and forensic science. This paper presents a learning-based single-image super-resolution method that uses a priori knowledge of the input as the plate images captured at poor quality and very low resolution. The proposed method employs a decision tree to classify the input image and the classification results are used to weight the image patches in the reconstruction step. Additionally, a histogram equalization is performed to improve the performance of the classifier. Experiments conducted on synthetic and real-world images demonstrate that the proposed method is capable of producing satisfactory results.