无需预训练模型即可训练图像生成模型，提升低剂量CT图像质量。
Poisson Flow Consistency Training
- 通过扰动核替代预训练模型，实现PFCM独立训练。
- 在低剂量CT图像去噪中，LPIPS与SSIM指标优于基线模型。
- 方法灵活可扩展，适用于多种生成任务，适合医疗图像处理研究者。
Poisson Flow Consistency Model（PFCM）基于成功的无条件图像生成与CT图像去噪模型PFGM++，但仅能通过知识蒸馏训练，限制了其在多模态数据中的应用。本文提出Poisson Flow Consistency Training（PFCT），首次实现PFCM的独立训练。通过引入扰动核替代预训练PFGM++，结合正弦离散化调度和Beta噪声分布，增强模型适应性并提升生成质量。在低剂量计算机断层扫描（CT）图像去噪任务中，该方法在LPIPS和SSIM指标上显著优于基线模型，效果接近一致性模型（Consistency Model）。PFCT被验证为有效且具有竞争力的训练方法，展现出在生成建模领域的广泛应用潜力，未来需进一步优化其参数并拓展至更多任务。该框架为构建PFCM提供了更高灵活性。
原文摘要 · Abstract (English)
The Poisson Flow Consistency Model (PFCM) is a consistency-style model based on the robust Poisson Flow Generative Model++ (PFGM++) which has achieved success in unconditional image generation and CT image denoising. Yet the PFCM can only be trained in distillation which limits the potential of the PFCM in many data modalities. The objective of this research was to create a method to train the PFCM in isolation called Poisson Flow Consistency Training (PFCT). The perturbation kernel was leveraged to remove the pretrained PFGM++, and the sinusoidal discretization schedule and Beta noise distribution were introduced in order to facilitate adaptability and improve sample quality. The model was applied to the task of low dose computed tomography image denoising and improved the low dose image in terms of LPIPS and SSIM. It also displayed similar denoising effectiveness as models like the Consistency Model. PFCT is established as a valid method of training the PFCM from its effectiveness in denoising CT images, showing potential with competitive results to other generative models. Further study is needed in the precise optimization of PFCT and in its applicability to other generative modeling tasks. The framework of PFCT creates more flexibility for the ways in which a PFCM can be created and can be applied to the field of generative modeling.
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