Papers › Implicit Image-to-Image Schrodinger Bridge for Image Restoration

Implicit Image-to-Image Schrodinger Bridge for Image Restoration

10 Mar 2024arXiv:2403.06069archive 2025-07-28

Yuang Wang, Siyeop Yoon, Pengfei Jin, Matthew Tivnan, Sifan Song, Zhennong Chen, Rui Hu, Li Zhang, Quanzheng Li, Zhiqiang Chen, Dufan Wu

Diffusion-based models have demonstrated remarkable effectiveness in image restoration tasks; however, their iterative denoising process, which starts from Gaussian noise, often leads to slow inference speeds. The Image-to-Image Schr\"odinger Bridge (I²SB) offers a promising alternative by initializing the generative process from corrupted images while leveraging training techniques from score-based diffusion models. In this paper, we introduce the Implicit Image-to-Image Schr\"odinger Bridge (I³SB) to further accelerate the generative process of I²SB. I³SB restructures the generative process into a non-Markovian framework by incorporating the initial corrupted image at each generative step, effectively preserving and utilizing its information. To enable direct use of pretrained I²SB models without additional training, we ensure consistency in marginal distributions. Extensive experiments across many image corruptions, including noise, low resolution, JPEG compression, and sparse sampling, and multiple image modalities, such as natural, human face, and medical images, demonstrate the acceleration benefits of I³SB. Compared to I²SB, I³SB achieves the same perceptual quality with fewer generative steps, while maintaining or improving fidelity to the ground truth.

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