License Plate Image Quality Enhancement Utilizing Super Resolution Generative Adversarial Networks

This thesis focuses primarily on enhancing the image quality of blurred license plates through the use of Super-Resolution Generative Adversarial Networks (SRGANs) [1]. We propose a synthetic dataset with SRGAN model to promote blurred image quality enhancement, and allow for model evaluation on a multitude of image input and output size combinations. SRGAN is mainly used for low-resolution image enhancement, but by heavily blurring the input images, the model is tested on its ability to blindly deblur and upsample images to the desired super-resolution (SR) size. The model enhances the image quality to nearly that of the reference images. The dataset consists of synthetic, high-resolution plates with character position and selection taken into account. The proposed SRGAN is evaluated on input sizes ranging from 16x16 to 64x64, and output sizes from 64x64 to 512x512. The images are compared with interpolated images of the same size combinations with Peak Signal-to-noise Ratio and Structural Similarity Index used as the evaluation metric