Linear Convergence of FISTA Iterates for Image Reconstruction Using Denoiser-Driven Regularization with Linear Denoisers

核心概念

This paper proves that the iterates of PnP-FISTA and RED-APG, two image reconstruction algorithms using denoiser-driven regularization, converge linearly to a unique solution when applied to linear inverse problems and using a class of linear denoisers.

摘要

Bibliographic Information:

Sinha, A., & Chaudhury, K. N. (2024). FISTA Iterates Converge Linearly for Denoiser-Driven Regularization. arXiv preprint arXiv:2411.10808v1.

Research Objective:

This paper investigates the iterate convergence of PnP-FISTA and RED-APG, two algorithms for image reconstruction that utilize denoiser-driven regularization. The authors aim to prove that these algorithms achieve global linear convergence when applied to linear inverse problems and using a specific class of linear denoisers.

Methodology:

The authors analyze the iterate convergence of PnP-FISTA and RED-APG by expressing the evolution of iterates as linear dynamical systems in higher dimensions. They leverage the properties of linear denoisers and spectral analysis to establish the convergence rate. The analysis focuses on symmetric denoisers initially and then extends to nonsymmetric denoisers by employing a scaled variant of the algorithm and operating in a modified Euclidean space.

Key Findings:

• The iterates of both PnP-FISTA and RED-APG exhibit global linear convergence to a unique reconstruction when applied to linear inverse problems like inpainting, deblurring, and superresolution.
• This linear convergence holds for a class of linear denoisers, including kernel denoisers like NLM and its symmetric variants, under verifiable assumptions.
• The analysis reveals that the spectral radius of a specific operator, derived from the forward model and denoising operator, plays a crucial role in determining the convergence rate.

Main Conclusions:

The study provides theoretical guarantees for the convergence of PnP-FISTA and RED-APG, demonstrating their effectiveness in solving linear inverse problems for image reconstruction when using linear denoisers. The established linear convergence rate offers insights into the algorithms' efficiency and provides a strong foundation for their practical application.

Significance:

This research contributes significantly to the field of image reconstruction by providing theoretical justification for the use of PnP-FISTA and RED-APG with linear denoisers. The proven linear convergence guarantees enhance the reliability and predictability of these algorithms, making them valuable tools for various computer vision applications.

Limitations and Future Research:

The analysis primarily focuses on linear denoisers. Future research could explore extending these convergence results to more sophisticated nonlinear denoisers, which are known to achieve state-of-the-art performance in image reconstruction. Investigating the convergence properties of PnP-FISTA and RED-APG with such advanced denoisers could further broaden their applicability and impact.

统计

For the deblurring experiment, an isotropic Gaussian blur of 25 × 25 size and standard deviation 1.6 was used, along with white Gaussian noise of standard deviation 0.03.
In the inpainting experiment, 30% of the pixels were randomly sampled.
The superresolution experiment involved a 2× downsampling factor.
White Gaussian noise with a standard deviation of 0.04 was added to the measurements in the superresolution experiment.

引用

"In this work, we demonstrate that using a class of data-driven linear denoisers, which are not state-of-the-art but still give good reconstructions [25, 27, 34], we can get strong convergence guarantees under fully verifiable assumptions."
"Overall, we observe a tradeoff between the regularization capacity of a denoiser and its convergence properties, a challenge that is widely recognized within the community [14, 18, 26, 36]."

从中提取的关键见解

FISTA Iterates Converge Linearly for Denoiser-Driven Regularization

by Arghya Sinha... 在 arxiv.org 11-19-2024

https://arxiv.org/pdf/2411.10808.pdf

更深入的查询

How could the insights from analyzing linear denoisers be applied to understand and potentially improve the convergence of PnP-FISTA and RED-APG with non-linear denoisers?

While the analysis presented in the paper focuses on linear denoisers, it offers valuable insights that could be extended to understand and improve the convergence of PnP-FISTA and RED-APG with non-linear denoisers. Here's how:

Local Linearization: Non-linear denoisers, especially those based on deep learning, often exhibit locally linear behavior. We can analyze the Jacobian of the denoising operator around a particular point in the image space. If the spectral properties of this Jacobian, which acts as a locally linearized version of the denoiser, satisfy conditions similar to Assumption 2.1, we might be able to guarantee local convergence.

Piecewise Linear Approximation: Complex non-linear denoisers can be approximated by piecewise linear functions. Each linear piece would have its own region of validity in the image space. By analyzing the spectral properties of these linear pieces and understanding how the algorithm transitions between these regions, we could gain insights into the overall convergence behavior.

Regularization through Spectral Constraints: The analysis highlights the importance of the spectral radius of the operator involving the denoiser and forward model (e.g., Pγ and Pμ,θ). We could explore incorporating spectral constraints during the training of non-linear denoisers. By encouraging the denoiser to have desirable spectral properties, we might be able to improve the convergence of PnP-FISTA and RED-APG.

Hybrid Approaches: Combining linear and non-linear denoisers in a hybrid framework could offer a practical approach. The linear denoiser, with its guaranteed convergence properties, could be used in the initial stages of the algorithm. As the iterates approach a good solution, we could switch to a more powerful non-linear denoiser for further refinement.

It's important to note that extending these insights to non-linear denoisers is not straightforward. The non-linearity introduces significant challenges in the analysis. However, the findings from the linear case provide a valuable starting point for further investigation.

Could the reliance on linear inverse problems limit the practical applicability of these findings, given that many real-world imaging scenarios involve non-linear image formation processes?

Yes, the reliance on linear inverse problems does limit the direct applicability of these findings to real-world imaging scenarios that often involve non-linear image formation processes. Here's why:

Model Mismatch: The analysis heavily relies on the linearity of the forward operator A. In non-linear imaging scenarios, using a linear model would introduce model mismatch, potentially leading to inaccurate reconstructions and invalidating the convergence guarantees.

Non-Convexity: Non-linear forward models often result in non-convex optimization problems. The convergence analysis presented in the paper, which is based on convex optimization principles, would not hold in such cases.

However, the findings are still relevant and can be indirectly applied to non-linear problems in the following ways:

Linearization: Similar to the previous answer, we can locally linearize the non-linear forward model around a particular point. This would allow us to apply the insights from the linear analysis locally. Iterative linearization techniques, commonly used in non-linear optimization, could benefit from these findings.

Understanding Convergence Behavior: While the specific convergence guarantees might not hold, the analysis provides a framework for understanding the factors influencing convergence in PnP and RED algorithms. The insights related to spectral properties and operator norms can still guide the design and analysis of algorithms for non-linear problems.

Benchmarking and Inspiration: The results for linear inverse problems serve as a benchmark for evaluating the performance of PnP-FISTA and RED-APG in more complex settings. Moreover, the analysis techniques and insights can inspire the development of new algorithms and theoretical frameworks for non-linear imaging problems.

In summary, while direct application to non-linear problems is limited, the findings provide valuable insights and a theoretical foundation that can guide future research in more general imaging scenarios.

If a trade-off exists between a denoiser's regularization capacity and its convergence properties, how can we develop new algorithms or denoisers that strike a better balance between these two aspects?

The trade-off between a denoiser's regularization capacity and its convergence properties is a crucial challenge in denoiser-driven regularization. Here are some strategies to develop algorithms and denoisers that strike a better balance:
Algorithm Development:

Adaptive Momentum: Instead of using a fixed momentum scheme, we can develop adaptive methods that adjust the momentum parameter (αk in the paper) based on the properties of the denoiser and the progress of the iterations. This could involve monitoring the spectral radius of the relevant operators or estimating the local Lipschitz constant of the denoiser.

Proximal Regularization: Incorporate an additional proximal term in the PnP or RED updates that explicitly encourages convergence. This proximal term could be designed to promote smoothness or sparsity in the iterates, thereby improving the algorithm's stability.

Multi-Stage Optimization: Employ a multi-stage optimization strategy where different denoisers or regularization strengths are used at different stages. We could start with a denoiser that prioritizes convergence and then gradually transition to a more powerful denoiser as the iterates approach a good solution.

Denoiser Design:

Spectral Regularization: During the training of deep learning-based denoisers, incorporate regularization terms that encourage desirable spectral properties. This could involve penalizing large spectral norms or promoting specific spectral distributions.

Convergence-Aware Training: Instead of solely focusing on denoising performance, train denoisers with objectives that explicitly consider their convergence behavior within PnP or RED frameworks. This could involve incorporating a measure of iterate convergence into the loss function.

Hybrid Denoisers: Design hybrid denoisers that combine the strengths of different approaches. For instance, we could combine a deep learning-based denoiser with a model-based denoiser that has well-understood convergence properties.

Interpretability and Control: Develop more interpretable deep learning denoisers that allow for better control over their spectral properties and local behavior. This would enable us to design denoisers that are both powerful and well-behaved in iterative algorithms.

Finding the optimal balance between regularization capacity and convergence is an ongoing research area. These strategies represent promising directions for developing more effective and reliable denoiser-driven regularization methods.