Arbitrary-steps Image Super-resolution via Diffusion Inversion (2412.09013v2)

Abstract: This study presents a new image super-resolution (SR) technique based on diffusion inversion, aiming at harnessing the rich image priors encapsulated in large pre-trained diffusion models to improve SR performance. We design a Partial noise Prediction strategy to construct an intermediate state of the diffusion model, which serves as the starting sampling point. Central to our approach is a deep noise predictor to estimate the optimal noise maps for the forward diffusion process. Once trained, this noise predictor can be used to initialize the sampling process partially along the diffusion trajectory, generating the desirable high-resolution result. Compared to existing approaches, our method offers a flexible and efficient sampling mechanism that supports an arbitrary number of sampling steps, ranging from one to five. Even with a single sampling step, our method demonstrates superior or comparable performance to recent state-of-the-art approaches. The code and model are publicly available at https://github.com/zsyOAOA/InvSR.

• The paper introduces a diffusion inversion technique that leverages large pre-trained models for enhanced image super-resolution.
• It employs a partial noise prediction strategy that initiates sampling from a noise-corrupted state to reduce complexity and maintain quality.
• Empirical results demonstrate improved PSNR, SSIM, and NIQE metrics, highlighting its efficiency and adaptability in real-world scenarios.

Overview of "Arbitrary-steps Image Super-resolution via Diffusion Inversion"

The paper "Arbitrary-steps Image Super-resolution via Diffusion Inversion" proposes a novel technique for image super-resolution (SR) leveraging diffusion inversion strategy and presents the InvSR method. This approach strategically exploits large pre-trained diffusion models to enhance SR task performance. The paper also introduces a Partial noise Prediction (PnP) strategy, which forms a crucial part of the proposed methodology.

In super-resolution tasks, the challenge lies in restoring high-resolution (HR) images from low-resolution (LR) counterparts, a fundamentally ill-posed problem exacerbated by unknown degradation models in real-world settings. Previous methods have utilized diffusion models, particularly those employing large text-to-image (T2I) networks, due to their robust generative capabilities and inherent understanding of image priors. The authors take this direction further by utilizing diffusion inversion to maximize the capabilities of these models.

Methodology

The InvSR approach comprises several innovative elements:

1. Diffusion Inversion Technique: The method reformulates diffusion inversion to specifically address the high fidelity demands of SR tasks. This involves using a deep neural network-based noise predictor to estimate optimal noise maps. Unlike conventional methods, InvSR does not modify the diffusion model’s architecture but instead innovates on the input to the model via noise parameter tuning.
2. Partial Noise Prediction Strategy: The PnP strategy proposes starting the sampling process from a noise-corrupted intermediate state rather than a complete noise map. This reduces computational complexity and retains fidelity by limiting the estimated noise to the initial diffusion steps and employing existing accelerated sampling techniques. The PnP method supports arbitrary numbers of sampling steps, thus offering the flexibility to adapt the sampling process to specific degradation conditions, making InvSR versatile in handling varying degrees of image degradation.
3. Training and Optimization: The training incorporates LPIPS and GAN losses alongside L2 loss to ensure perceptually pleasing results. The noise predictor's architecture is based on the encoder of VQGAN, enhanced with self-attention layers, facilitating efficient learning of the noise map.

Results

The empirical evaluations conducted on both synthetic and real-world datasets show promising results. For instance, InvSR achieves strong performance across multiple image quality assessment metrics, including PSNR, SSIM, and NIQE, when compared to state-of-the-art SR methods. It particularly excels in environments with high noise where a single or fewer sampling steps may suffice, further showcasing its adaptability.

The paper highlights the scalability and real-world applicability of InvSR by quantifying performance in relation to the number of sampling steps, achieving efficient SR even when constrained to a single step without compromising on quality. The method’s capability to balance perceptual image quality and computational efficiency is especially notable.

Implications and Future Work

The paper's contributions potentially streamline the use of diffusion models in SR, marking a significant step towards practical implementations of such models. The flexibility inherent in InvSR’s design allows it to manage various degradation scenarios, laying a foundation for robust application in real-world conditions.

Future research directions could involve optimizing the runtime further for scenarios demanding high efficiency, such as mobile or real-time applications. Additionally, investigating the adaptation of InvSR's principles into other domains of image enhancement or restoration may provide broader benefits, given its demonstrated efficiency in utilizing and augmenting learned diffusion-based image priors. Integrating quantitative model efficiency measures and refinement techniques, such as model compression or quantization, could pave the way for even more practical implementations.