# Removal of Color-Document Image Show-Through Based on Self-Supervised Learning
###### Abstract  
Show-through phenomena have always been a challenging issue in color-document image processing, which is widely used in various fields such as finance, education, and administration. Existing methods for processing color-document images face challenges, including dealing with double-sided documents with show-through effects, accurately distinguishing between foreground and show-through parts, and addressing the issue of insufficient real image data for supervised training. To overcome these challenges, this paper proposes a self-supervised-learning-based method for removing show-through effects in color-document images. The proposed method utilizes a two-stage-structured show-through-removal network that incorporates a double-cycle consistency loss and a pseudo-similarity loss to effectively constrain the process of show-through removal. Moreover, we constructed two datasets consisting of different show-through mixing ratios and conducted extensive experiments to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method achieves competitive performance compared to state-of-the-art methods and can effectively perform show-through removal without the need for paired datasets. Specifically, the proposed method achieves an average PSNR of 33.85 dB on our datasets, outperforming comparable methods by a margin of 0.89 dB. document image restoration; show-through removal; self-supervised learning; cycle generative adversarial network +
Footnote †: journal: Applied  
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Footnote †: journal: Applied  
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Footnote 1: institutetext: School of Computer and Control Engineering, Yantai University, Yantai 264005, China; [EMAIL_ADDRESS] (M.N. ); [EMAIL_ADDRESS] (J.X.) 2
Footnote 1: institutetext: School of Computer and Control Engineering, Yantai University, Yantai 264005, China; [EMAIL_ADDRESS] (J.X.) 2
Footnote 1: institutetext: School of Computer and Control Engineering, Yantai University, Yantai 264005, China; nimengying@ytu. Here, \(H(\cdot)\) represents the transfer function, describing the transformations undergone by the image during the acquisition and transmission processes, while \(n\) denotes the presence of noise, representing the existence of random interference. Due to the diverse causes of image degradation, different IR tasks are closely associated with their corresponding transmission functions \(H(\cdot)\) and noises \(n\). For instance, in the task of document image dehazing, the transfer function \(H(\cdot)\) can represent optical properties, color distortions, and other phenomena introduced during scanning, capturing, or other image-acquisition methods. The noise term represents random interference introduced during the processes of image acquisition and transmission. These transformations may result in the occurrence of a \"show-through\" phenomenon in document images, making text, graphics, colors, and other information visible through the pages or paper. Based on the appearance and underlying mechanisms of the show-through images, the document image show-through model can be expressed as follows:  
\[I^{S}=(1-\alpha)\cdot I^{GT}+\alpha\cdot\phi(S) \tag{2}\]  
In this context, \(I^{S}\) represents the mixed image with a show-through effect; \(I^{GT}\) represents the foreground image (the front side of the document image); \(S\) represents the background image (the back side of the document image); and \(\alpha\) represents the blending ratio of the show-through layer. Additionally, \(\phi(\cdot)\) denotes the bleeding-attenuation function. Resolving Equation (2) without any prior knowledge leads to an infinite number of solutions due to the unknown variables \(S\), \(\alpha\), and \(\phi(\cdot)\), making the restoration of \(I^{GT}\) from \(I^{S}\) a non-deterministic problem.