I$^2$SB: Image-to-Image Schrödinger Bridge
This work addresses image restoration for computer vision applications, offering a more interpretable and scalable approach, though it is incremental as it builds on existing diffusion models.
The paper tackles image restoration tasks like inpainting and super-resolution by proposing I^2SB, a new class of conditional diffusion models that directly learn nonlinear diffusion processes between degraded and clean images, achieving performance matching or surpassing standard methods without requiring knowledge of corruption operators.
We propose Image-to-Image Schrödinger Bridge (I$^2$SB), a new class of conditional diffusion models that directly learn the nonlinear diffusion processes between two given distributions. These diffusion bridges are particularly useful for image restoration, as the degraded images are structurally informative priors for reconstructing the clean images. I$^2$SB belongs to a tractable class of Schrödinger bridge, the nonlinear extension to score-based models, whose marginal distributions can be computed analytically given boundary pairs. This results in a simulation-free framework for nonlinear diffusions, where the I$^2$SB training becomes scalable by adopting practical techniques used in standard diffusion models. We validate I$^2$SB in solving various image restoration tasks, including inpainting, super-resolution, deblurring, and JPEG restoration on ImageNet 256x256 and show that I$^2$SB surpasses standard conditional diffusion models with more interpretable generative processes. Moreover, I$^2$SB matches the performance of inverse methods that additionally require the knowledge of the corruption operators. Our work opens up new algorithmic opportunities for developing efficient nonlinear diffusion models on a large scale. scale. Project page and codes: https://i2sb.github.io/