Implicit Image-to-Image Schrodinger Bridge for CT Super-Resolution and
  Denoising
Yuang Wang Siyeop Yoon Pengfei Jin Matthew Tivnan Zhennong Chen Rui Hu Li Zhang Zhiqiang Chen Quanzheng Li Dufan Wu
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 Schr\"odinger 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.