Implicit Image-to-Image Schrodinger Bridge for Image Restoration
<|reference_start|>Implicit Image-to-Image Schrodinger Bridge for Image Restoration: Diffusion-based models are widely recognized for their effectiveness in image restoration tasks; however, their iterative denoising process, which begins from Gaussian noise, often results in slow inference speeds. The Image-to-Image Schr\"odinger Bridge (I$^2$SB) presents a promising alternative by starting the generative process from corrupted images and leveraging training techniques from score-based diffusion models. In this paper, we introduce the Implicit Image-to-Image Schr\"odinger Bridge (I$^3$SB) to further accelerate the generative process of I$^2$SB. I$^3$SB reconfigures the generative process into a non-Markovian framework by incorporating the initial corrupted image into each step, while ensuring that the marginal distribution aligns with that of I$^2$SB. This allows for the direct use of the pretrained network from I$^2$SB. Extensive experiments on natural images, human face images, and medical images validate the acceleration benefits of I$^3$SB. Compared to I$^2$SB, I$^3$SB achieves the same perceptual quality with fewer generative steps, while maintaining equal or improved fidelity to the ground truth.<|reference_end|>
@article{wang2024implicit,
    title={Implicit Image-to-Image Schrodinger Bridge for Image Restoration},
    author={Yuang Wang, Siyeop Yoon, Pengfei Jin, Matthew Tivnan, Sifan Song,
  Zhennong Chen, Rui Hu, Li Zhang, Quanzheng Li, Zhiqiang Chen and Dufan Wu},
    journal={arXiv preprint arXiv:2403.06069},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06069},
    primaryClass={eess.IV cs.CV cs.LG}
}