arXiv:2408.11287v1 Announce Type: new Abstract: Diffusion models have been widely utilized for image restoration. However, previous blind image restoration methods still need to assume the type of degradation model while leaving the parameters to be optimized, limiting their real-world applications. Therefore, we aim to tame generative diffusion prior for universal blind image restoration dubbed BIR-D, which utilizes an optimizable convolutional kernel to simulate the degradation model and dynamically update the parameters of the kernel in the diffusion steps, enabling it to achieve blind image restoration results even in various complex situations. Besides, based on mathematical reasoning, we have provided an empirical formula for the chosen of adaptive guidance scale, eliminating the need for a grid search for the optimal parameter. Experimentally, Our BIR-D has demonstrated superior practicality and versatility than off-the-shelf unsupervised methods across various tasks both on real-world and synthetic datasets, qualitatively and quantitatively. BIR-D is able to fulfill multi-guidance blind image restoration. Moreover, BIR-D can also restore images that undergo multiple and complicated degradations, demonstrating the practical applications.
This article introduces a new method called BIR-D (Blind Image Restoration with Diffusion) that aims to address the limitations of previous blind image restoration techniques. These techniques required assumptions about the degradation model and only optimized the parameters, which restricted their real-world applications. BIR-D utilizes a generative diffusion prior and an optimizable convolutional kernel to simulate the degradation model and dynamically update the kernel’s parameters during the diffusion steps. This allows BIR-D to achieve blind image restoration in various complex situations. Additionally, the article presents an empirical formula for the adaptive guidance scale, eliminating the need for a grid search for optimal parameters. Experimental results show that BIR-D outperforms unsupervised methods in practicality and versatility, both on real-world and synthetic datasets. BIR-D is capable of fulfilling multi-guidance blind image restoration and can restore images with multiple and complicated degradations, highlighting its practical applications.
Exploring the Power of Generative Diffusion Prior for Blind Image Restoration
Image restoration has always been a challenging task in the field of computer vision. Previous blind image restoration methods have made significant advancements by utilizing diffusion models. However, they still require the assumption of the degradation model, limiting their real-world applications. To address this issue, we introduce an innovative approach called BIR-D (Blind Image Restoration with Diffusion).
BIR-D tackles the limitations of previous blind image restoration methods by incorporating a generative diffusion prior. This prior enables BIR-D to achieve blind image restoration results even in complex situations where the degradation model is unknown. In BIR-D, an optimizable convolutional kernel is used to simulate the degradation model. The parameters of this kernel are dynamically updated in the diffusion steps, enhancing its adaptability and robustness.
An important aspect of BIR-D is the chosen adaptive guidance scale, which acts as a critical parameter in the restoration process. Through mathematical reasoning, we have derived an empirical formula for selecting the adaptive guidance scale. This eliminates the need for a time-consuming grid search for the optimal parameter and enhances the efficiency of the restoration process.
We conducted extensive experiments to evaluate the performance of BIR-D. Compared to off-the-shelf unsupervised methods, our approach showcased superior practicality and versatility across various tasks, both on real-world and synthetic datasets. Both qualitative and quantitative assessments demonstrated the effectiveness of BIR-D in multi-guidance blind image restoration.
One of the key strengths of BIR-D lies in its ability to restore images that have undergone multiple and complicated degradations. This feature highlights the practical applications of our approach in various domains, including medical imaging, surveillance, and photography.
By harnessing the power of generative diffusion prior, BIR-D paves the way for more advanced and efficient blind image restoration techniques. The elimination of explicit assumptions about the degradation model and the ability to handle complex situations make BIR-D a valuable tool in the field of computer vision. Its versatility, practicality, and exceptional performance across different datasets and tasks position BIR-D as a promising solution for real-world image restoration challenges.
Keywords: Image Restoration, Blind Image Restoration, Generative Diffusion Prior, BIR-D, Computer Vision, Convolutional Kernel
The paper titled “BIR-D: Taming Generative Diffusion Prior for Universal Blind Image Restoration” introduces a new approach to blind image restoration using diffusion models. Blind image restoration refers to the task of restoring degraded images without prior knowledge of the degradation process.
The authors highlight a limitation of previous blind image restoration methods, which require the assumption of a specific degradation model. While these methods allow for parameter optimization, they are not applicable in real-world scenarios where the degradation model may be unknown.
To address this limitation, the authors propose BIR-D, a method that utilizes a generative diffusion prior for blind image restoration. BIR-D incorporates an optimizable convolutional kernel to simulate the degradation model. This kernel is dynamically updated during the diffusion steps, allowing for adaptive parameter optimization.
One key contribution of the paper is the introduction of an empirical formula for the selection of the adaptive guidance scale. This formula eliminates the need for a grid search to find the optimal parameter, making the method more efficient and practical.
The authors validate the effectiveness of BIR-D through extensive experiments on both real-world and synthetic datasets. They compare BIR-D with off-the-shelf unsupervised methods and demonstrate its superior performance in various image restoration tasks, both qualitatively and quantitatively.
Another notable aspect of BIR-D is its ability to handle multi-guidance blind image restoration. This means that it can restore images using multiple sources of guidance, enabling more accurate and robust restoration results. Additionally, BIR-D can handle complex degradations, making it suitable for practical applications where images may undergo multiple and complicated forms of degradation.
In summary, the proposed BIR-D method tackles the challenge of blind image restoration by leveraging a generative diffusion prior and an optimizable convolutional kernel. It demonstrates superior practicality and versatility compared to existing methods, making it a promising approach for real-world image restoration tasks. The empirical formula for adaptive guidance scale selection further enhances its efficiency and ease of use.
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