Document Image Restore via SPADE-Based Super-Resolution Network

With the development of deep learning technology, various structures and research methods for the super-resolution restoration of natural images and document images have been introduced. In particular, a number of recent studies have been conducted and developed in image restoration using generative adversarial networks. Super-resolution restoration is an ill-posed problem because of some complex restraints, such as many high-resolution images being restored for the same low-resolution image, as well as difficulty in restoring noises such as edges, light smudging, and blurring. In this study, we applied super-resolution restoration to text images using the spatially adaptive denormalization (SPADE) structure, different from previous methods. This paper used SPADE for document image restoration to solve previous problems such as edge unclearness, hardness to catch features of texts, and the image color transition. As a result of this study, it can be confirmed that the edge of the character and the ambiguous stroke are restored more clearly when contrasting with the other previously suggested methods. Additionally, the proposed method's PSNR and SSIM scores are 8% and 15% higher compared to the previous methods.