Two-stage Visible Watermark Removal Model Based on Global and Local Features for Document Images
Visible watermark is a common digital image copyright protection measure.Analysis of the removal results of water-marks can verify the effectiveness of the watermarks on images and provide reference and inspiration for watermark designers to design or add them.Currently,most watermark removal methods are based on research on natural images,while document images are also widely used in daily life.However,due to the lack of publicly available datasets for removing watermarks from document images,research on removing watermarks from such images is relatively limited.To explore the effectiveness of watermark re-moval methods on document images,a dataset for removing watermarks from single document images,the single document image watermark removal dataset(SDIWRD),is constructed.In the research on the removal of watermarks in document images,it is found that the removal results of existing watermark removal methods often leave watermark artifacts,such as main body arti-facts or outline artifacts.To address this problem,a two-stage watermark removal model based on global and local features is pro-posed,which uses a two-stage half-instance normalized encoder-decoder architecture from coarse to fine.In the coarse stage,a global and local feature extraction module is designed to enhance the capture of global spatial features while preserving the extrac-tion of local detail information,thus helping with watermark removal.In the fine stage,the fine network shares the weights of the coarse stage and constructs a recurrent feature fusion module to fully explore the important features of the coarse stage encoder and provide rich context information for the fine stage,helping with detailed watermark removal.In addition,a structure similarity loss is used to improve the visual quality of the removed watermark.The proposed method is tested on the SDIWRD dataset,and the results show that the peak signal-to-noise ratio(PSNR)is 41.21dB,the structural similarity(SSIM)is 99.07％,and the root mean square error(RMSE)is 3.64,which are better than existing methods.In addition,the proposed method is also tested on the publicly available CLWD color watermark removal dataset,and the results showethat the PSNR is 39.31 dB,the SSIM is 98.81％,and the RMSE is 3.50,which are also better than existing watermark removal methods.These experimental results demonstrate that the proposed method has good generalization and can effectively alleviate the problem of watermark artifacts.Fi-nally,some suggestions for preventing watermark removal are also proposed.The proposed method and dataset can be publicly accessed at the corresponding website.
Visible watermarkWatermark removalGlobal and local feature extractionRecurrent feature fusionDocument image
万方数据
维普
