Ship License Plate Super Resolution in the Wild

Ship license plate (SLP) recognition plays an important role in ship supervision and harbour management. Practically, low-resolution (LR) SLP images are illegible and challenging to SLP recognition. Most existing super-resolution (SR) methods are not suitable for real-world LR SLP images with over smoothed reconstructions. To alleviate these deficiencies, in this letter, we propose a parallel enhanced SR generative adversarial network (PESRGAN) for SLP images. A novel degradation model is developed to construct a more feasible LR dataset. A parallel SR convolutional neural network (SRCNN) module based on ESRGAN is proposed for feature extraction. To characterize the difference between text foreground and background, a new gradient loss is developed in PESRGAN to sharpen the character boundary. Comparisons to many state-of-the-art (SOTA) SR methods are presented to show the effectiveness of the proposed algorithm.