Towards Blind Image Restoration with Generative Diffusion Prior

Основні поняття

DiffBIR decouples the blind image restoration problem into two stages: 1) degradation removal and 2) information regeneration, and leverages the generative ability of latent diffusion models to achieve state-of-the-art performance for blind super-resolution, blind face restoration, and blind image denoising tasks.

Анотація

The paper presents DiffBIR, a general restoration pipeline that can handle different blind image restoration tasks in a unified framework. DiffBIR decouples the blind image restoration problem into two stages:

Degradation removal: Removing image-independent content using task-specific restoration modules.
Information regeneration: Generating the lost image content using a generation module that leverages the generative ability of latent diffusion models.

The generation module, called IRControlNet, is trained based on specially produced condition images without distracting noisy content for stable generation performance. Additionally, the authors introduce a region-adaptive restoration guidance that can modify the denoising process during inference without model re-training, allowing users to balance realness and fidelity through a tunable guidance scale.
Extensive experiments have demonstrated DiffBIR's superiority over state-of-the-art approaches for blind image super-resolution, blind face restoration, and blind image denoising tasks on both synthetic and real-world datasets.

Статистика

DiffBIR decouples blind image restoration problem into two stages: degradation removal and information regeneration.
DiffBIR leverages the generative ability of latent diffusion models to achieve state-of-the-art performance for blind super-resolution, blind face restoration, and blind image denoising tasks.
The authors introduce a region-adaptive restoration guidance that can modify the denoising process during inference without model re-training, allowing users to balance realness and fidelity.

Цитати

"DiffBIR decouples blind image restoration problem into two stages: 1) degradation removal: removing image-independent content; 2) information regeneration: generating the lost image content."
"We propose IRControlNet that leverages the generative ability of latent diffusion models to generate realistic details."
"We introduce a training-free controllable module - region-adaptive restoration guidance that performs in sampling process, for achieving flexible trade-off between quality and fidelity for various user preferences."

Ключові висновки, отримані з

DiffBIR

by Xinqi Lin,Ji... о arxiv.org 04-02-2024

https://arxiv.org/pdf/2308.15070.pdf

Глибші Запити

How can the proposed two-stage pipeline be extended to other blind image restoration tasks beyond the three covered in this work

The proposed two-stage pipeline in the DiffBIR framework can be extended to other blind image restoration tasks by adapting the restoration modules and the generation module to suit the specific characteristics of the new tasks. For instance, for tasks like blind image deblurring, the restoration module can be trained to remove motion blur or defocus blur, while the generation module can focus on regenerating sharp details. Similarly, for tasks like blind image inpainting, the restoration module can be designed to fill in missing regions, while the generation module can generate realistic content to complete the image. By customizing the restoration and generation modules for different types of degradations and restoration requirements, the two-stage pipeline can be effectively applied to a wide range of blind image restoration tasks.

What are the potential limitations of the region-adaptive restoration guidance approach, and how could it be further improved

The region-adaptive restoration guidance approach in DiffBIR may have limitations in scenarios where the image content is complex and contains a mix of high-frequency and low-frequency regions. In such cases, the guidance scale may need to be finely tuned to balance between maintaining fidelity in low-frequency regions and preserving details in high-frequency regions. Additionally, the effectiveness of the guidance approach may be impacted by the quality of the high-fidelity guidance image used for comparison. To improve this approach, one potential enhancement could be the integration of a dynamic guidance scale adjustment mechanism that automatically adapts to the content of the image being restored. This dynamic adjustment could be based on the analysis of gradient magnitudes or other image features to optimize the restoration guidance for each specific image.

What other types of generative models, beyond latent diffusion models, could be explored for the information regeneration stage of the DiffBIR framework

Beyond latent diffusion models, other types of generative models that could be explored for the information regeneration stage of the DiffBIR framework include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Autoregressive Models. VAEs can be used to learn a latent space representation of the image content and generate realistic details based on this representation. GANs can generate high-quality images by training a generator network to produce images that are indistinguishable from real images. Autoregressive models, such as PixelCNN, can generate images pixel by pixel, capturing complex dependencies in the image data. By incorporating these different generative models into the information regeneration stage, DiffBIR can benefit from a diverse range of generative capabilities and potentially improve the quality and realism of the restored images.