Abstract When documents are scanned and converted into digital images, they often contain unwanted and unevenly superimposed show-through layers, which directly impact the performance of other vision-related tasks. The show-through removal is significant in image processing applications. Show-through refers to the presence of transparency or uneven materials in document images during the scanning or capturing process, resulting in a visible transparency effect. In this study, we propose a two-stage framework called document show-through removal generative adversarial networks that utilizes conditional generative adversarial networks to restore document images affected by show-through phenomena. The coarse stage consists of two parallel generative architectures (Gremove and Gpredict). We employ a residual block structure between the encoder and decoder of Gremove to extract deeper features. In Gpredict, we utilize a deep residual attention block based on an attention mechanism, substituting the traditional convolutional layer. Finally, the results are fused in the refinement stage (Grefine) to generate higher-quality document images. Experimental results demonstrate that our method not only effectively removes show-through information but also significantly enhances the clarity of foreground content. Compared to state-of-the-art methods, our approach achieves a better performance, such as 0.47 dB in peak signal-to-noise ratio. The source code is available at https://github.com/GongJiaXin694/DSR-GAN.
Gong et al. (Thu,) studied this question.