PSO-WESRGAN: a novel document image super resolution

Abstract

Abstract One of the major challenges of document images that can hinder readability and the analysis of information is low resolution; this is typically caused by low-pixel density scanning or excessive compression to save storage space. This results in a loss of fine detail in images, making it difficult to detect critical information. To solve these problems, super-resolution techniques are used. These techniques improve image quality by increasing the resolution while maintaining the fine detail. PSO-WESRGAN is an innovative method that combines wavelet processing, deep-transfer learning, and particle swarm optimization (PSO). Wavelet processing analyzes image detail at diverse scales and orientations, while transfer-based deep-learning advantages pre-trained models on vast image data sets. By integrating PSO, the efficiency of the method is enhanced through the optimal exploration of the solution space to identify the best parameters for the super-resolution model. The experimental results show the effectiveness of this method and open up prospects for future improvements in the super-resolution of document images.