Learning Efficient and Effective Trajectories for Differential Equation-based Image Restoration

• URL: http://arxiv.org/abs/2410.04811v1
• Date: Mon, 7 Oct 2024 07:46:08 GMT
• Title: Learning Efficient and Effective Trajectories for Differential Equation-based Image Restoration
• Authors: Zhiyu Zhu, Jinhui Hou, Hui Liu, Huanqiang Zeng, Junhui Hou,
• Abstract summary: We reformulate the trajectory optimization of this kind of method, focusing on enhancing both reconstruction quality and efficiency. We propose cost-aware trajectory distillation to streamline complex paths into several manageable steps with adaptable sizes. Experiments showcase the significant superiority of the proposed method, achieving a maximum PSNR improvement of 2.1 dB over state-of-the-art methods.
• Score: 59.744840744491945
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The differential equation-based image restoration approach aims to establish learnable trajectories connecting high-quality images to a tractable distribution, e.g., low-quality images or a Gaussian distribution. In this paper, we reformulate the trajectory optimization of this kind of method, focusing on enhancing both reconstruction quality and efficiency. Initially, we navigate effective restoration paths through a reinforcement learning process, gradually steering potential trajectories toward the most precise options. Additionally, to mitigate the considerable computational burden associated with iterative sampling, we propose cost-aware trajectory distillation to streamline complex paths into several manageable steps with adaptable sizes. Moreover, we fine-tune a foundational diffusion model (FLUX) with 12B parameters by using our algorithms, producing a unified framework for handling 7 kinds of image restoration tasks. Extensive experiments showcase the significant superiority of the proposed method, achieving a maximum PSNR improvement of 2.1 dB over state-of-the-art methods, while also greatly enhancing visual perceptual quality. Project page: \url{https://zhu-zhiyu.github.io/FLUX-IR/}.

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