A learning-based single-image super-resolution method for very low quality license plate images

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.