Abstract
Conditional diffusion models have gained recognition for their effectiveness in image restoration tasks, yet their iterative denoising process, starting from Gaussian noise, often leads to slow inference speeds. As a promising alternative, the Image-to-Image Schrödinger Bridge (I2SB) initializes the generative process from corrupted images and integrates training techniques from conditional diffusion models. In this study, we extended the I2SB method by introducing the Implicit Image-to-Image Schrodinger Bridge (I3SB), transitioning its generative process to a non-Markovian process by incorporating corrupted images in each generative step. This enhancement empowers I3SB to generate images with better texture restoration using a small number of generative steps. The proposed method was validated on CT super-resolution and denoising tasks and outperformed existing methods, including the conditional denoising diffusion probabilistic model (cDDPM) and I2SB, in both visual quality and quantitative metrics. These findings underscore the potential of I3SB in improving medical image restoration by providing fast and accurate generative modeling.
Abstract (translated)
条件扩散模型因在图像修复任务中的有效性而获得了认可，然而其从高斯噪声开始的迭代去噪过程通常会导致较慢的推理速度。作为一种有前途的替代方法，图像到图像的Schrödinger桥（I2SB）从损坏的图像中初始化生成过程，并整合了条件扩散模型的训练技术。在本文中，我们通过引入I3SB方法，将生成过程从损坏的图像中进行扩展，并通过在每个生成步骤中包含损坏图像来将其转化为非马尔可夫过程。这一改进使得I3SB能够通过较少的生成步骤来生成具有更好纹理修复效果的图像。所提出的方法在CT超分辨率去噪任务中进行了验证，并在视觉质量和数量指标上优于现有的方法，包括条件去噪扩散概率模型（cDDPM）和I2SB。这些发现强调了我们所提出的I3SB通过提供快速和准确的生成建模来改善医学图像修复的潜力。
URL
https://arxiv.org/abs/2403.06069
