Constructing an Interpretable Deep Denoiser by Unrolling Graph Laplacian Regularizer

Основні поняття

A general framework to construct an interpretable graph-based deep denoiser (GDD) by unrolling a solution to a maximum a posteriori (MAP) problem equipped with a graph Laplacian regularizer (GLR) as signal prior.

Анотація

The paper proposes a general framework to construct an interpretable graph-based deep denoiser (GDD) in three main steps:

1. Select a trusted (pseudo-)linear denoiser Ψ with known good denoising performance, such as the bilateral filter (BF).

2. Approximate the corresponding graph Laplacian matrix L = μ^-1(Ψ^-1 - I_N) using truncated Taylor series expansion (TSE), leveraging a recent theorem that maps any (pseudo-)linear denoiser Ψ to an equivalent graph filter for a MAP denoising problem with GLR as prior.

3. Solve the linear system (I_N + μL)x^* = y to compute the denoised output x^* by unrolling the conjugate gradient (CG) algorithm into a feed-forward network (FFN), which is amenable to end-to-end parameter tuning.

The resulting GDD network is "graph-interpretable", low in parameter count, and easy to initialize thanks to L derived from a known well-performing denoiser Ψ. Experimental results show that GDD achieves competitive image denoising performance compared to competitors, while employing far fewer parameters, and is more robust to covariate shift.

Статистика

The paper does not provide any specific numerical data or statistics. The key results are presented in the form of PSNR values and visual comparisons of denoised images.

Цитати

"Theorem 1 establishes, under mild conditions, a one-to-one mapping between any (pseudo-)linear denoiser Ψ and a corresponding graph filter (7) that is a solution to MAP problem (6) specified by Laplacian L."
"Experimental results show that GDD achieves competitive image denoising performance compared to competitors, but employing far fewer parameters, and is more robust to covariate shift."

Ключові висновки, отримані з

Constructing an Interpretable Deep Denoiser by Unrolling Graph Laplacian Regularizer

by Seyed Alirez... о arxiv.org 09-11-2024

https://arxiv.org/pdf/2409.06676.pdf

Глибші Запити

How can the proposed GDD framework be extended to other image restoration tasks beyond denoising, such as super-resolution or inpainting?

The proposed Graph-based Deep Denoiser (GDD) framework can be effectively extended to other image restoration tasks, such as super-resolution and inpainting, by leveraging its underlying graph signal processing principles and the flexibility of the algorithm unrolling approach.

Super-Resolution: In super-resolution, the goal is to reconstruct a high-resolution image from a low-resolution input. The GDD framework can be adapted by modifying the graph Laplacian regularizer to incorporate spatial relationships and pixel dependencies that are crucial for high-frequency detail recovery. By defining a new graph structure that emphasizes the connectivity between low-resolution and high-resolution pixel locations, the GDD can be trained to learn the mapping from low to high resolution. The conjugate gradient (CG) algorithm can still be employed to solve the optimization problem, but the loss function would need to reflect the differences between the reconstructed high-resolution image and the ground truth.

Inpainting: For inpainting, where the objective is to fill in missing or corrupted parts of an image, the GDD framework can utilize the graph Laplacian regularizer to promote smoothness and coherence in the filled regions. The graph can be constructed to connect known pixels with their unknown neighbors, allowing the GDD to learn from the surrounding context. The optimization process can be adjusted to minimize the difference between the inpainted image and the known pixel values, while also ensuring that the inpainted regions maintain consistency with the overall image structure.

Generalization of Features: In both tasks, the feature vector used in the graph construction can be expanded to include additional characteristics relevant to the specific restoration task, such as texture information or edge gradients. This allows the GDD to adapt to the unique requirements of super-resolution and inpainting, enhancing its performance across various image restoration applications.

What are the potential limitations or drawbacks of the graph Laplacian regularizer compared to other signal priors, and how can they be addressed?

While the graph Laplacian regularizer (GLR) offers several advantages, such as promoting smoothness and leveraging local pixel relationships, it also has potential limitations compared to other signal priors:

Sensitivity to Graph Construction: The performance of the GLR heavily depends on the quality of the graph construction. If the graph does not accurately represent the underlying image structure, the denoising or restoration results may suffer. To address this, one can incorporate adaptive graph construction techniques that dynamically adjust the graph based on the input image characteristics, such as using local pixel statistics or learned features to define edges and weights.

Computational Complexity: The computation of the graph Laplacian and its inverse can be computationally intensive, especially for large images. This can be mitigated by employing efficient algorithms for sparse matrix operations or approximating the graph Laplacian using techniques like low-rank approximations, which can reduce the computational burden while maintaining performance.

Limited Expressiveness: Compared to more complex signal priors, such as deep learning-based methods that can capture intricate patterns, the GLR may be less expressive. To enhance expressiveness, one could combine the GLR with other priors, such as total variation or learned neural network features, creating a hybrid model that benefits from the strengths of multiple approaches.

Can the interpretability of the GDD network be further enhanced by incorporating domain-specific knowledge or constraints into the graph construction and optimization process?

Yes, the interpretability of the GDD network can be significantly enhanced by integrating domain-specific knowledge and constraints into the graph construction and optimization process. Here are several strategies to achieve this:

Domain-Specific Features: By incorporating features that are relevant to the specific application domain (e.g., texture descriptors for natural images or edge information for medical imaging), the graph can be constructed to reflect the unique characteristics of the data. This not only improves the performance of the GDD but also makes the model's decisions more interpretable, as the features directly relate to the domain knowledge.

Constraints on Graph Structure: Introducing constraints based on prior knowledge about the relationships between pixels can guide the graph construction. For instance, in medical imaging, anatomical knowledge can inform which pixels should be connected, leading to a more meaningful graph representation. This can help in understanding how the model processes information and makes decisions.

Visualization of Graph Properties: Enhancing interpretability can also involve visualizing the graph properties, such as edge weights and connectivity patterns. By analyzing how these properties change during the optimization process, researchers can gain insights into the model's behavior and the importance of different features in the restoration task.

Explainable AI Techniques: Incorporating explainable AI techniques, such as attention mechanisms or saliency maps, can further enhance interpretability. These techniques can highlight which parts of the input image are most influential in the denoising or restoration process, providing a clearer understanding of the model's decision-making.

By integrating these strategies, the GDD framework can not only improve its performance across various image restoration tasks but also enhance its interpretability, making it a valuable tool for practitioners in fields requiring transparent and reliable image processing solutions.