Restoring degraded images has long been a significant focus in image preprocessing, aiming to recover high-quality images from their degraded counterparts. While numerous studies have addressed specific types of degradation, All-In-One methods have achieved a notable milestone by enabling a single model to handle various degradations without requiring prior knowledge of the degradation types. However, these approaches often fall short of delivering satisfactory performance in complex and multiple degradation scenarios. To address these challenges, this paper proposes a Multi-Prompt Guided Diffusion Network for All-In-One image restoration, referred to as MPGNet. MPGNet consists of two key components: the Image Restoration Part (IRP) and the Prompt Generation Part (PGP). The IRP employs a diffusion model to perform the image restoration task, while the PGP generates tailored prompts to guide image restoration from degradation. By leveraging meticulously designed prompts, MPGNet empowers the diffusion network to adaptively handle complex and multiple degradation scenarios. To enhance adaptability to various degradation types, content specificity, and overall performance, we design three types of prompts: Style Prompt, Content Prompt, and Learnable Prompt. The first two prompts are carefully generated using the Image Encoder from the pre-trained vision-text model CLIP, with contrastive learning applied during respective training, while the Learnable Prompt is custom-designed separately. We also introduce a Prompt Encoder that integrates Style, Content, and Learnable Prompts into a unified representation, facilitating image restoration for IRP. The experimental results demonstrate that the proposed method significantly outperforms recent approaches in both single and multiple degradation scenarios.
Li et al. (Sat,) studied this question.