# Diffusion-Denoising Process with Gated U-Net for High-Quality Document Binarization
## 2 Related Works  
### Document Binarization  
Document binarization aims to conduct pixel-wise binary classification of the background and text within a document image. The methods for document binarization can be categorized into two main groups: traditional binarization algorithm-based methods and deep learning-based methods. Traditional binarization algorithms rely on various nonparametric thresholding techniques. These methods achieve binary classification by applying a threshold to distinguish between the background and text. [PERSON]'s method [6], for instance, uses a global thresholding approach that maximizes the separation between the background and text, seeking the highest interclass variation for binary classification. These global thresholding methods have proven effective for document images with uniform degradation but may not be well suited for document images with non-uniform degradation. To address this challenge, pixel-wise local thresholding methods have been introduced. Local thresholding methods enhance classification accuracy by considering the relationships between neighboring pixels based on local statistical information [5; 7]. Although these algorithm-based binarization methods excel at handling uniform noise, they  
Figure 3: The architecture of the proposed model for document binarization. The network architecture can be divided into the pixel space and latent space, and the diffusion-denoising process proceeds according to the time step in the latent space.  
still encounter challenges when dealing with documents containing diverse non-uniform noise such as overwriting [8].  
To address these issues, deep learning-based methods have gained significant attention recently. An iterative neural network was developed to generate clean, binarized images from non-uniformly degraded images, and [PERSON]'s algorithm was applied to enhance the resulting images [8]. Binarization was achieved by training mid-level representations through an encoder-decoder architecture composed of convolutional neural networks [27]. This encoder-decoder architecture was also used to construct a network for selective output [28]. A cascading U-Net architecture was proposed for tackling complex document image-processing tasks [29]. [PERSON] et al. [30] leveraged convolutional neural networks to identify foreground pixels using input-generated multichannel images. Furthermore, generative models based on GANs, which treat document binarization as an image-to-image task, have been introduced [31].  
[PERSON] et al. [15] proposed a model for generating clean, binarized images through multi-scale information fusion based on conditional generative adversarial networks (cGANs). Additionally, in [17], cGANs were employed, demonstrating performance improvements in tasks such as watermark removal, deblurring, and binarization. [PERSON] et al. [32] introduced a three-stage method for binarization by integrating a discrete wavelet transform and GANs. Lastly, color-independent adversarial networks have been developed in two stages to capture both the global and local features of documents [18].