# Cascaded Dual-Inpainting Network for Scene Text
## 2 Related Work
### Image Inpainting
However, it was difficult to effectively inpaint images with large damaged areas or complex backgrounds. [PERSON] et al. [10] developed a method that integrates an auxiliary encoder and contextual reconstruction loss, ensuring enhanced coherence between inpainted regions and the global image. The auxiliary encoder optimized feature representation in the refinement network to produce more natural restoration results. [PERSON] et al. [11] introduced the Discrete Wavelet Transform (DWT) into the generator architecture, achieving an optimal balance between preserving high-frequency details and enhancing perceptual quality. This approach successfully resolved the inherent frequency conflict issues in conventional GAN-based image inpainting methods. [PERSON] et al. [12] proposed a GAN-based framework that integrated textual features through adversarial loss and text-matching loss, effectively guiding the image generation consistent with text descriptions. This work established the theoretical foundation for subsequent multimodal generation research. [PERSON] et al. [13] proposed a text-guided dual-modal attention mechanism that combined with image contextual information to infer missing content, and it enhanced the generator's capability through an auxiliary pathway and multi-loss functions. [PERSON] et al. [14] proposed a dual-stage generator for coarse-to-fine image inpainting, which integrated a dual-attention module to capture both sentence-level and word-level textual features. This approach effectively resolved semantic misalignment and detail deficiency in text-guided image inpainting. [PERSON] et al. [15] proposed an image inpainting approach incorporating a Visual-Textual Multimodal Fusion Module (VTM-FM), which integrated textual and visual features through cross-modal attention mechanisms. This method significantly improved GAN performance in inpainting via contextual information, frequency-domain decomposition, and cross-modal fusion. GANs have achieved improvements in generation quality, detail restoration, and semantic coherence through adversarial training while offering an adaptable generation framework applicable to diverse tasks. However, GANs suffer from inherent limitations: training instability, high computational cost, inadequate semantic comprehension capabilities in multimodal contexts, etc. Denoising Diffusion Probabilistic Models (DDPMs) perform image inpainting through two distinct stages: the forward diffusion process (gradual noise addition) and the reverse denoising process (iterative noise removal). [PERSON] et al. [16] proposed a framework that enabled data generation through gradual noise addition and iterative noise removal. It had a stable training process and high-quality generative capabilities. [PERSON] et al. [17] proposed refinements for the DDPM, which included learning reverse process variances, optimizing the noise schedule, reducing gradient noise, improving the sampling speed, and reducing diffusion steps. These improvements enhanced the model's ability to capture complex data distributions, reduced computational costs, and increased sampling efficiency. [PERSON] et al. [18] introduced Imagen, a text-to-image diffusion model built on the capabilities of large transformer language models for text understanding and hinged on the strength of diffusion models in high-fidelity image generation. This approach effectively mitigated detail degradation caused by high guidance weights through dynamic thresholding and static threshold adaptation while significantly enhancing both output diversity and visual fidelity in generated images. [PERSON] et al. [19] proposed imagen editor, a cascaded diffusion architecture that progressively refined outputs. This approach enhanced performance in high-resolution image editing and image inpainting. [PERSON] et al. [20] proposed Stable Diffusion, which operated the diffusion process in the latent space to reduce computational complexity, optimize perceptual loss, and improve sampling methodologies.