SPIRE: Semantic Prompt-Driven Image Restoration (2312.11595v2)

Abstract: Text-driven diffusion models have become increasingly popular for various image editing tasks, including inpainting, stylization, and object replacement. However, it still remains an open research problem to adopt this language-vision paradigm for more fine-level image processing tasks, such as denoising, super-resolution, deblurring, and compression artifact removal. In this paper, we develop SPIRE, a Semantic and restoration Prompt-driven Image Restoration framework that leverages natural language as a user-friendly interface to control the image restoration process. We consider the capacity of prompt information in two dimensions. First, we use content-related prompts to enhance the semantic alignment, effectively alleviating identity ambiguity in the restoration outcomes. Second, our approach is the first framework that supports fine-level instruction through language-based quantitative specification of the restoration strength, without the need for explicit task-specific design. In addition, we introduce a novel fusion mechanism that augments the existing ControlNet architecture by learning to rescale the generative prior, thereby achieving better restoration fidelity. Our extensive experiments demonstrate the superior restoration performance of SPIRE compared to the state of the arts, alongside offering the flexibility of text-based control over the restoration effects.

• The paper introduces a dual-prompt system that decouples semantic content from restoration instructions to precisely recover degraded images.
• It leverages text-driven diffusion models integrated with ControlNet to address semantic ambiguities and degradation challenges.
• Experimental results show improved FID and LPIPS scores, demonstrating superior perceptual quality over state-of-the-art methods.

Overview of TIP: Text-Driven Image Processing with Semantic and Restoration Instructions

This paper presents a novel framework named TIP (Text-driven Image Processing) that leverages text-based prompts to perform a variety of image restoration tasks. The authors introduce an innovative approach that allows users to employ natural language instructions to guide the image processing operation, offering a flexible and user-friendly interface to manipulate both semantic content and degradation-specific restoration.

Methodology

TIP is built upon the rapid advancements in text-driven diffusion models, which traditionally have been applied to high-level image editing tasks but have not been extensively explored for fine-level image restoration. The contribution of TIP lies in its ability to discern between semantic ambiguities and degradation types, which are common challenges in image restoration. Degraded images may lack clear content interpretation, causing existing models to struggle with identity ambiguities or to misinterpret aesthetic photographic effects as distortions.

The framework is structured around a dual-prompt system that decouples the text input into semantic-level content prompts and restoration instructions. This dual approach is facilitated by a novel integration with a ControlNet-based architecture. ControlNet enables the system to handle semantic descriptions and restoration parameters, employing a fusion mechanism that enhances restoration fidelity.

Experimental Results

The authors conducted extensive experiments demonstrating TIP's superior performance over existing state-of-the-art models in various settings. The experiments underscore the approach's flexibility and robustness across different types of degradations and restoration requirements:

1. Blind Restoration: TIP was tested with general prompts like "remove all degradations", operating similarly to blind restoration models but with better control over semantic and degradation ambiguity.
2. Semantic Restoration: By providing explicit semantic prompts, TIP effectively retained the identity of ambiguous objects, which traditional models often misinterpreted or averaged out.
3. Task-Specific Restoration: TIP's ability to process task-specific instructions was highlighted, showcasing its adaptability through text-based specifications of restoration type and strength.

Numerically, TIP exhibited enhanced FID and LPIPS scores, showing improvements in perceptual quality and image fidelity when compared to both traditional and contemporary approaches, such as StableSR and SwinIR.

Technical Implications

The framework introduces a unified model for dealing with diverse image restoration tasks directly instructed by text, bridging a gap in multimodal image processing. This approach suggests that text-driven interfaces can significantly enhance the controllability of image-based AI applications. The modular nature of TIP, allowing for independent semantic and restoration prompt processing, could lead to broader applications in other domains requiring customizable output generation.

Future Directions

The modular design and adaptability of TIP open pathways for several potential future developments:

• Expansion to Video Processing: Extending the text-driven restoration approach from images to video sequences, preserving temporal consistency could be explored.
• Integration with Larger Vision-LLMs: Leveraging larger LLMs could refine the specificity and variety of restoration prompts and improve robustness.
• Personalized Image Enhancement: Developing more personalized restoration strategies tailored to individual user preferences and styles through more nuanced language prompts.

In summary, the TIP framework represents a significant advancement in text-to-image processing, effectively integrating semantic control and restoration precision into a cohesive system. This contribution has broad implications for future research in multimodal AI, offering a foundation for more intuitive and interactive image restoration solutions.