Unlimited-Size Diffusion Restoration (2303.00354v1)

Abstract: Recently, using diffusion models for zero-shot image restoration (IR) has become a new hot paradigm. This type of method only needs to use the pre-trained off-the-shelf diffusion models, without any finetuning, and can directly handle various IR tasks. The upper limit of the restoration performance depends on the pre-trained diffusion models, which are in rapid evolution. However, current methods only discuss how to deal with fixed-size images, but dealing with images of arbitrary sizes is very important for practical applications. This paper focuses on how to use those diffusion-based zero-shot IR methods to deal with any size while maintaining the excellent characteristics of zero-shot. A simple way to solve arbitrary size is to divide it into fixed-size patches and solve each patch independently. But this may yield significant artifacts since it neither considers the global semantics of all patches nor the local information of adjacent patches. Inspired by the Range-Null space Decomposition, we propose the Mask-Shift Restoration to address local incoherence and propose the Hierarchical Restoration to alleviate out-of-domain issues. Our simple, parameter-free approaches can be used not only for image restoration but also for image generation of unlimited sizes, with the potential to be a general tool for diffusion models. Code: https://github.com/wyhuai/DDNM/tree/main/hq_demo

• The paper introduces Mask-Shift Restoration to effectively overcome boundary artifacts in patch-based image processing.
• It presents Hierarchical Restoration, a two-phase approach that boosts global semantic coherence in zero-shot image restoration.
• Experimental results demonstrate that the methods outperform existing models in tasks like super-resolution and denoising.

Unlimited-Size Diffusion Restoration: An Expert Overview

The paper "Unlimited-Size Diffusion Restoration" introduces notable advancements in the application of diffusion models for zero-shot image restoration (IR). These models, widely appreciated for their capacity to address various IR tasks without finetuning, previously grappled with limitations concerning image size. This work effectively extends their applicability to images of arbitrary sizes while preserving the advantages inherent to zero-shot methods.

Key Contributions

The primary contributions of this research are twofold. First, the authors propose Mask-Shift Restoration (MSR), a method that mitigates boundary artifacts when processing images as patches. Second, they introduce Hierarchical Restoration (HiR), aimed at addressing out-of-domain issues and enhancing global semantic coherence. Both techniques are parameter-free, training-free, and flexible enough to accommodate a wide range of applications, including super-resolution, colorization, inpainting, and denoising.

Mask-Shift Restoration

MSR emerges as a straightforward yet effective solution to the challenges posed by dividing an image into patches. Typically, processing fixed-size patches independently leads to artifacts due to the disregard for global semantics and adjacent patch information. MSR cleverly leaves overlapping regions between patches as known segments, which are then treated as mask constraints in subsequent patches. This approach ensures cohesiveness across patches and eliminates boundary artifacts.

Hierarchical Restoration

HiR tackles the limitations in semantic restoration when dealing with large images. It employs a two-phase approach. Initially, a smaller version of the image is restored to capture global information, referred to as the semantic restoration phase. This low-resolution outcome serves as a guiding prior for the subsequent texture restoration phase, where detail and coherence are refined. HiR significantly improves the semantic quality of restored images by integrating global priors.

Methodology and Theoretical Foundations

The paper leverages concepts from Range-Null space Decomposition (RND) within the Denoising Diffusion Null-space Model (DDNM) to tackle image restoration tasks. It combines these methods with the proposed MSR and HiR to ensure robustness in diverse applications. This integration exemplifies a thoughtful reconciliation of diffusion sampling approaches with theoretical foundations in linear inverse problems.

Experimental Validation

The authors validate their methods using diffusion models pre-trained on ImageNet and demonstrate their approach's efficacy in both qualitative and quantitative assessments. The proposed techniques outperform existing models, such as BSRGAN, in terms of realness and consistency, especially in tasks involving super-resolution and noise reduction.

Implications and Future Work

This research extends the toolbox for zero-shot image restoration, addressing real-world scenarios where image size can vary dramatically. The proposed methods hold promise for future applications involving progressively evolving diffusion models. However, challenges persist, such as computational efficiency and dependency on pre-trained model strengths. Future work may focus on optimizing these methods for non-linear inverse problems and integrating them with latent space models like those used in Stable Diffusion.

In conclusion, this paper advances the field of zero-shot image restoration by providing methodologies that enhance diffusion models' capabilities in handling images of arbitrary sizes while ensuring quality and consistency. The insights presented are valuable not only for theoretical investigations but also for practical applications in various domains utilizing diffusion models.