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Prompt-Guided State-Space Foundation Model for Real-World Image Restoration
The field of image restoration is continually evolving with the introduction of advanced deep learning models capable of tackling increasingly complex restoration tasks. The use of foundation models, which are pre-trained on diverse data before being fine-tuned for specific tasks, has demonstrated considerable promise in various domains of artificial intelligence. This proposal aims to develop a new foundation model for image restoration by incorporating the state-space model and enhancing it with text prompt capabilities. This approach will allow the model to perform targeted restorations based on descriptive textual prompts, significantly improving the precision and quality of the restoration process.
Keywords: Text prompt, state-space model, foundation model, image restoration
The evolution of image restoration foundation models has been marked by significant milestones, particularly with the advent of deep learning. Initially, convolutional neural networks (CNNs) dominated the field, offering substantial improvements over traditional methods. However, the introduction of transformers revolutionized the approach to image restoration. With their ability to handle long-range dependencies and model complex patterns, transformers provided a new paradigm for addressing image degradation. Despite their success, transformers are not without limitations; they often require large amounts of data for training and can be computationally intensive, which limits their practicality for real-time applications.
In response to these challenges, the state-space model and Mamba architecture have emerged as promising alternatives. The state-space model, known for its efficiency in modeling dynamic systems, has been adapted for image restoration tasks, offering a balance between performance and computational demands. The Mamba architecture, in particular, leverages the state-space model’s strengths to provide a scalable and efficient solution for image restoration, capable of handling high-resolution images and complex degradation patterns with relative ease.
The integration of textual prompts into image restoration models represents a groundbreaking shift towards more intuitive human-AI interactions. Text prompts allow users to convey their intentions and desired outcomes in natural language, making the restoration process more accessible and user-friendly. This capability is especially beneficial in scenarios where specific restoration goals are difficult to articulate through traditional interfaces, enabling a more collaborative and flexible approach to image enhancement.
The goal of this research is to develop a foundation model that harnesses the computational efficiency of the Mamba architecture and the intuitive guidance of textual prompts to set a new standard in image restoration. By combining these technologies, we aim to create a model that not only excels in restoring images but also aligns closely with user intentions, ultimately bridging the gap between advanced image processing techniques and everyday usability.
The evolution of image restoration foundation models has been marked by significant milestones, particularly with the advent of deep learning. Initially, convolutional neural networks (CNNs) dominated the field, offering substantial improvements over traditional methods. However, the introduction of transformers revolutionized the approach to image restoration. With their ability to handle long-range dependencies and model complex patterns, transformers provided a new paradigm for addressing image degradation. Despite their success, transformers are not without limitations; they often require large amounts of data for training and can be computationally intensive, which limits their practicality for real-time applications.
In response to these challenges, the state-space model and Mamba architecture have emerged as promising alternatives. The state-space model, known for its efficiency in modeling dynamic systems, has been adapted for image restoration tasks, offering a balance between performance and computational demands. The Mamba architecture, in particular, leverages the state-space model’s strengths to provide a scalable and efficient solution for image restoration, capable of handling high-resolution images and complex degradation patterns with relative ease.
The integration of textual prompts into image restoration models represents a groundbreaking shift towards more intuitive human-AI interactions. Text prompts allow users to convey their intentions and desired outcomes in natural language, making the restoration process more accessible and user-friendly. This capability is especially beneficial in scenarios where specific restoration goals are difficult to articulate through traditional interfaces, enabling a more collaborative and flexible approach to image enhancement.
The goal of this research is to develop a foundation model that harnesses the computational efficiency of the Mamba architecture and the intuitive guidance of textual prompts to set a new standard in image restoration. By combining these technologies, we aim to create a model that not only excels in restoring images but also aligns closely with user intentions, ultimately bridging the gap between advanced image processing techniques and everyday usability.
1. To Design an Integrated System: Develop a foundation model that seamlessly integrates the computational efficiency of the Mamba architecture with the intuitive guidance of textual prompts for image restoration tasks.
2. To Enhance User Interaction: Create a user-friendly interface that allows non-expert users to guide the image restoration process through natural language prompts, making the technology more accessible.
3. To Improve Restoration Quality: Achieve superior image restoration quality by leveraging the Mamba architecture’s ability to capture long-range dependencies and complex patterns
within images.
4. To Expand Applicability: Ensure that the model is versatile enough to handle a wide range of image restoration tasks, from common issues like noise reduction and deblurring to more
complex challenges such as inpainting and super-resolution.
5. To Optimize Computational Efficiency: Address the computational limitations of existing models by utilizing the linear complexity of the Mamba architecture, enabling the processing of high-resolution images in a timely manner.
6. To Validate Model Effectiveness: Conduct extensive testing and validation to demonstrate the model’s effectiveness and superiority over current state-of-the-art methods in various real-world scenarios.
7. To Foster Collaborative Development: Encourage collaboration within the research community by sharing insights, methodologies, and potentially the model itself, to spur further
innovation in the field of image restoration. These objectives aim to push the boundaries of what’s possible in image restoration, making it more efficient, user-centric, and widely applicable.
1. To Design an Integrated System: Develop a foundation model that seamlessly integrates the computational efficiency of the Mamba architecture with the intuitive guidance of textual prompts for image restoration tasks.
2. To Enhance User Interaction: Create a user-friendly interface that allows non-expert users to guide the image restoration process through natural language prompts, making the technology more accessible.
3. To Improve Restoration Quality: Achieve superior image restoration quality by leveraging the Mamba architecture’s ability to capture long-range dependencies and complex patterns within images.
4. To Expand Applicability: Ensure that the model is versatile enough to handle a wide range of image restoration tasks, from common issues like noise reduction and deblurring to more complex challenges such as inpainting and super-resolution.
5. To Optimize Computational Efficiency: Address the computational limitations of existing models by utilizing the linear complexity of the Mamba architecture, enabling the processing of high-resolution images in a timely manner.
6. To Validate Model Effectiveness: Conduct extensive testing and validation to demonstrate the model’s effectiveness and superiority over current state-of-the-art methods in various real-world scenarios.
7. To Foster Collaborative Development: Encourage collaboration within the research community by sharing insights, methodologies, and potentially the model itself, to spur further innovation in the field of image restoration. These objectives aim to push the boundaries of what’s possible in image restoration, making it more efficient, user-centric, and widely applicable.
Please include your CV and transcript in the submission.
**Yawei Li**
https://yaweili.bitbucket.io/
yawei.li@vision.ee.ethz.ch
Please include your CV and transcript in the submission.
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