CTSRMV: A Chinese Text Image Super-Resolution Method Based on Mobile ViT
Zhouxin Lu, Ben Wang, Liangqi Chen, Lingbo Hao, Keyong Hu · 2022 4th International Conference on Communications, Information System and Computer Engineering (CISCE) · 2022
A large number of text images come from natural scenes. However, most of them are taken by non professional cameras such as mobile phones, and their resolution is low, which greatly reduces the readability of the text in the images. The text image super-resolution method aims to enhance the low resolution image to obtain a clearer text image. At present, with the appearance of convolutional neural network, image super-resolution technology has developed rapidly. The traditional super-resolution method is mainly used for natural scene images, and is usually not suitable for text. Many scholars have proposed super-resolution network methods for text images, but most of them are based on English text. Due to the complexity and diversity of Chinese texts, these networks are not suitable for Chinese texts. In order to improve the shortcomings of the above methods, a Chinese text dataset is generated, and a text-image super-resolution lightweight network based on Mobile ViT is also proposed. Experiments show that our proposed dataset CTW is meaningful for the study of Chinese text image super-resolution. And the new method has better performance in PSNR/SSIM metrics and can effectively restore blurred low-resolution images into clear high-resolution images.