Leveraging Diffusion Prior and Domain Shift for Efficient and High-Performance Image Super-Resolution

Основні поняття

A novel diffusion-based super-resolution model, DoSSR, that leverages the generative power of pretrained diffusion models while significantly enhancing inference efficiency through a domain shift strategy and customized stochastic differential equation solvers.

Анотація

The paper presents DoSSR, a diffusion-based image super-resolution (SR) framework that aims to strike an optimal balance between efficiency and performance. The key contributions are:

Formulation of a novel diffusion equation that models the SR task as a gradual shift from the low-resolution (LR) domain to the high-resolution (HR) domain. This "domain shift" integration allows the diffusion process to start from LR images rather than random noise, boosting inference efficiency.

Expansion of the discrete domain shift process to a continuous formulation using stochastic differential equations (SDEs), termed as DoS-SDEs. This enables the design of customized fast solvers for efficient sampling.

Empirical evaluation on both synthetic and real-world datasets demonstrates that DoSSR achieves state-of-the-art performance while requiring only 5 sampling steps, resulting in a 5-7 times speedup compared to previous diffusion-based SR methods.

The paper first provides an overview of the limitations in existing diffusion-based SR approaches, which either neglect the potential of pretrained diffusion models or compromise inference efficiency. It then details the proposed DoSSR framework, including the domain shift diffusion equation, the DoS-SDEs formulation, and the customized fast solvers.
Extensive experiments show that DoSSR outperforms current state-of-the-art methods on various benchmarks in terms of both quantitative metrics and visual quality, while notably achieving significantly faster inference speeds. The ablation studies further validate the effectiveness of the key components, such as the domain shift guidance and the selection of the starting point for inference.

Статистика

Diffusion-based SR models often struggle to strike an optimal balance between efficiency and performance.
Prevailing diffusion models either neglect to exploit the potential of existing extensive pretrained models or necessitate dozens of forward passes starting from random noises, compromising inference efficiency.
DoSSR achieves a remarkable speedup of 5-7 times compared to previous diffusion-based SR methods.

Цитати

"Diffusion-based image super-resolution (SR) models have attracted substantial interest due to their powerful image restoration capabilities. However, prevailing diffusion models often struggle to strike an optimal balance between efficiency and performance."
"To tackle this challenge, we propose DoSSR, a Domain Shift diffusion-based SR model. We initially view the SR task as a gradual shift from the LR domain to the HR domain, describing this transition with a linear equation, which is called domain shift equation."
"Experimental results demonstrate that our proposed method achieves state-of-the-art performance on synthetic and real-world datasets, while notably requiring only 5 sampling steps. Compared to previous diffusion prior based methods, our approach achieves a remarkable speedup of 5-7 times, demonstrating its superior efficiency."

Ключові висновки, отримані з

Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs

by Qinpeng Cui,... о arxiv.org 09-27-2024

https://arxiv.org/pdf/2409.17778.pdf

Глибші Запити

How can the proposed domain shift strategy be extended to other inverse problems beyond image super-resolution, such as image denoising or inpainting?

The proposed domain shift strategy in the DoSSR framework can be effectively extended to other inverse problems like image denoising and inpainting by leveraging the core principles of domain transition and diffusion processes. In image denoising, the domain shift can be conceptualized as transitioning from a noisy image domain to a clean image domain. By defining a domain shift equation similar to that used in DoSSR, we can model the gradual reduction of noise as a diffusion process. This would involve conditioning the diffusion model on the noisy input image, allowing the model to learn the mapping from the noisy domain to the clean domain through a series of diffusion steps.
For inpainting, the domain shift strategy can be adapted to handle missing or corrupted regions in an image. The process would involve defining a domain shift that transitions from an incomplete image domain (with missing pixels) to a complete image domain. By utilizing a pretrained diffusion model, the inpainting task can be framed as a conditional generation problem where the model learns to fill in the missing areas based on the context provided by the surrounding pixels. The domain shift equation can guide the model in understanding how to interpolate and generate plausible content in the missing regions, thus enhancing the quality of the inpainted images.

What are the potential limitations or drawbacks of the domain shift approach, and how can they be addressed to further improve the performance and robustness of the model?

One potential limitation of the domain shift approach is its reliance on the quality of the pretrained diffusion models. If the pretrained model is not well-suited for the specific characteristics of the target domain, the performance may suffer. To address this, fine-tuning strategies can be employed, where the model is further trained on a dataset that closely resembles the target domain, ensuring that the learned representations are more aligned with the specific features of the images being processed.
Another drawback is the potential for artifacts or inconsistencies in the generated images, particularly when the domain shift is not well-defined or when the model encounters out-of-distribution samples. To mitigate this, incorporating robust training techniques such as adversarial training or using a diverse set of training data can help the model generalize better to unseen scenarios. Additionally, implementing a feedback mechanism that allows the model to iteratively refine its outputs based on quality assessments could enhance the robustness of the generated images.

Given the advancements in efficient diffusion sampling, how can the DoSSR framework be adapted to enable real-time or interactive applications of image super-resolution in various domains, such as video processing or computational photography?

To adapt the DoSSR framework for real-time or interactive applications, several strategies can be implemented. First, optimizing the sampling process is crucial. By leveraging the fast solvers developed for the DoS-SDEs, the framework can be designed to minimize the number of function evaluations required for generating high-resolution images. This can be achieved by implementing adaptive sampling techniques that dynamically adjust the number of steps based on the complexity of the input image, allowing for quicker processing times without sacrificing quality.
Second, integrating the DoSSR framework with hardware acceleration, such as utilizing GPUs or specialized AI chips, can significantly enhance processing speeds. By optimizing the model architecture for parallel processing, the framework can handle multiple images or frames simultaneously, making it suitable for video processing applications.
Lastly, incorporating user interactivity features, such as real-time previews or adjustable parameters for the super-resolution process, can enhance the user experience in computational photography. This could involve developing a user-friendly interface that allows users to see immediate results and make adjustments on-the-fly, thereby making the DoSSR framework applicable in scenarios where quick feedback is essential, such as live video streaming or interactive editing tools.