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ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting (2307.12348v3)

Published 23 Jul 2023 in cs.CV

Abstract: Diffusion-based image super-resolution (SR) methods are mainly limited by the low inference speed due to the requirements of hundreds or even thousands of sampling steps. Existing acceleration sampling techniques inevitably sacrifice performance to some extent, leading to over-blurry SR results. To address this issue, we propose a novel and efficient diffusion model for SR that significantly reduces the number of diffusion steps, thereby eliminating the need for post-acceleration during inference and its associated performance deterioration. Our method constructs a Markov chain that transfers between the high-resolution image and the low-resolution image by shifting the residual between them, substantially improving the transition efficiency. Additionally, an elaborate noise schedule is developed to flexibly control the shifting speed and the noise strength during the diffusion process. Extensive experiments demonstrate that the proposed method obtains superior or at least comparable performance to current state-of-the-art methods on both synthetic and real-world datasets, even only with 15 sampling steps. Our code and model are available at https://github.com/zsyOAOA/ResShift.

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Authors (3)
  1. Zongsheng Yue (22 papers)
  2. Jianyi Wang (14 papers)
  3. Chen Change Loy (288 papers)
Citations (118)
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Summary

An Analysis of ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting

The paper "ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting" introduces an innovative approach aimed at improving the efficiency and quality of image super-resolution (SR) tasks. The researchers from Nanyang Technological University focus on addressing the limitations in current diffusion-based SR methods, particularly the excessive inference time due to numerous sampling steps.

Core Contributions

The authors propose a novel diffusion model leveraging a residual shifting mechanism between low-resolution (LR) and high-resolution (HR) images. This approach substantially reduces the diffusion steps required, enhancing inference speed without sacrificing performance. The model transitions from a distribution based on the LR image, enabling iterative recovery of the HR image while maintaining computational efficiency.

Key innovations include:

  1. Markov Chain Construction: The model efficiently transitions between HR and LR images by shifting their residuals. This design eliminates the need for post-acceleration techniques, which often degrade output quality.
  2. Noise Schedule: An adaptable noise schedule is devised to manage the shifting speed and noise levels during diffusion, accommodating a trade-off between fidelity and realism in the results.

The authors substantiate their claims through extensive experimentation, demonstrating superior or comparable performance to state-of-the-art (SotA) methods even with minimal sampling steps.

Methodology

The core of the technique involves a shorter Markov chain tailored for SR. Unlike typical diffusion models starting from Gaussian noise, ResShift initiates from a distribution aligned with the LR image. This transition is governed by a novel transition kernel iteratively shifting the residual, facilitating rapid convergence.

An analytical expression of the evidence lower bound is derived, informing the optimization objectives. The researchers also implement a noise schedule offering precise control, highlighting its flexibility across various configurations.

Experimental Evaluation

Compared to SotA methods like BSRGAN and LDM, ResShift achieves enhanced PSNR and LPIPS metrics, indicative of better fidelity and perceptual quality. Testing on both synthetic and real-world datasets corroborates these findings. Notably, with only 15 sampling steps, ResShift matches or surpasses models requiring hundreds of steps, proving its efficiency.

Quantitatively, the model maintains a low runtime while improving on non-reference metrics like CLIPIQA and MUSIQ, which assess image realism. The paper confirms ResShift’s potential in real-world applications, although challenges remain in handling highly degraded inputs.

Implications and Future Directions

ResShift signifies a pivotal advancement in balancing computational efficiency and output quality in diffusion-based SR models. Its methodological innovations, particularly in reducing sampling steps, present a blueprint for further refining generative models.

Future research could explore enhancements in training data to better emulate real-world degradation, improving model robustness across diverse scenarios. Additionally, the integration of more advanced noise scheduling could fine-tune the balance between speed and accuracy, fostering broader applications in real-time image processing.

In conclusion, ResShift offers a promising pathway in SR research, addressing key inefficiencies in traditional methods while opening avenues for further exploration in efficient generative modeling.