Deblurring via Stochastic Refinement (2112.02475v2)

Abstract: Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These metrics are known to be poorly correlated with human perception, and often lead to unrealistic reconstructions. We present an alternative framework for blind deblurring based on conditional diffusion models. Unlike existing techniques, we train a stochastic sampler that refines the output of a deterministic predictor and is capable of producing a diverse set of plausible reconstructions for a given input. This leads to a significant improvement in perceptual quality over existing state-of-the-art methods across multiple standard benchmarks. Our predict-and-refine approach also enables much more efficient sampling compared to typical diffusion models. Combined with a carefully tuned network architecture and inference procedure, our method is competitive in terms of distortion metrics such as PSNR. These results show clear benefits of our diffusion-based method for deblurring and challenge the widely used strategy of producing a single, deterministic reconstruction.

• The paper introduces a novel stochastic refinement method that fuses deterministic prediction with conditional diffusion to generate diverse and plausible deblurred images.
• It pioneers a technique to navigate the perception-distortion trade-off by adjusting noise and sampling parameters, achieving superior perceptual metrics.
• The approach reduces computational costs through an initial predictor, enabling efficient sampling and state-of-the-art performance on benchmark datasets.

Deblurring via Stochastic Refinement: An Expert Analysis

This paper introduces a novel approach for image deblurring by leveraging conditional diffusion models, presenting a significant shift from deterministic models predominately used in the field. The presented framework emphasizes perceptual quality over conventional metrics like PSNR, addressing the disparity between these metrics and human perception.

Key Contributions

1. Stochastic Refinement with Diffusion Models: The authors propose a "predict-and-refine" strategy wherein a deterministic predictor first generates an initial deblurred image, which is subsequently refined by a stochastic sampler. This novel use of a conditional diffusion model is designed to produce multiple plausible solutions for the deblurring problem, thereby effectively capturing the ill-posed nature of this inverse problem.
2. Perception-Distortion Trade-off: The paper demonstrates a new methodology to traverse the perception-distortion (P-D) trade-off curve using a single model. By altering the noise schedule and sampling parameters during inference, the model can be optimized either for perceptual quality or for distortion metrics, contributing a versatile tool for deblurring tasks.
3. Efficient Sample Generation: By integrating an initial predictor, the computational cost associated with the iterative sampling process of diffusion models is reduced. This enhancement achieves competitive sampling rates without sacrificing the quality of the generated samples, which is critical in practical applications.

Numerical Results and Claims

The proposed method outperforms state-of-the-art deblurring techniques on several perceptual metrics across benchmark datasets such as GoPro and HIDE. Quantitatively, it achieves superior FID and LPIPS scores, which are well-regarded indicators of perceptual similarity to reference images.

Specifically:

• The method achieves a 70% improvement in FID compared to competitive methods, demonstrating a marked enhancement in generating perceptually authentic images.
• Utilizing the proposed "sample averaging," the model achieves top-tier PSNR values, showcasing its ability to maintain high fidelity under traditional distortion metrics.

Implications

Practically, the adoption of this approach in consumer electronics could elevate user satisfaction by enabling visually pleasing results even in challenging scenarios, such as motion blur in mobile photography. The reduction in computational expense made possible by the initial predictor is particularly relevant for real-time applications.

Theoretically, this work challenges the status quo of using deterministic models for deblurring, proposing a probabilistic framework that aligns more closely with the inherently ambiguous nature of the task. The concept of using conditional diffusion models in such settings may inspire further research into even more complex image restoration problems like super-resolution and inpainting.

Future Developments

Looking ahead, optimizing the efficiency of diffusion models remains a promising area of focus. Techniques such as reducing the number of required sampling steps without degrading the sample quality, as well as exploring other neural architectures that further reduce computational demand, could enhance the applicability of these models in resource-constrained environments.

Furthermore, integrating advanced learning paradigms such as adversarial training with these diffusion approaches could potentially reconcile the generative capabilities with the fidelity required for perceptual authenticity.

In summary, this paper sets a new precedent for image deblurring by advocating for stochastic reconstruction frameworks, and through rigorous empirical validation, positions its approach at the forefront of perception-oriented image restoration techniques.