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Implicit Image-to-Image Schrödinger Bridge for Image Restoration.

Yuang Wang^1,2, Siyeop Yoon², Pengfei Jin²

¹The Department of Engineering Physics, Tsinghua University, 30 Shuangqing Road, Haidian, Beijing, 100084, China.

Pattern Recognition

|April 21, 2025

Summary

This summary is machine-generated.

Implicit Image-to-Image Schrödinger Bridge (I3SB) accelerates image restoration by using corrupted images directly in its generative process. This method achieves comparable quality to existing models but with significantly faster inference speeds.

Keywords:

Diffusion Model Image Restoration Schrödinger Bridge

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Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Diffusion models excel at image restoration but suffer from slow inference due to iterative denoising from Gaussian noise.
Image-to-Image Schrödinger Bridge (I2SB) improves initialization by using corrupted images, drawing from score-based diffusion model training.
Existing methods face challenges with inference speed, limiting practical applications in real-time image restoration.

Purpose of the Study:

To introduce the Implicit Image-to-Image Schrödinger Bridge (I3SB) for accelerated image restoration.
To enhance the generative process of I2SB by incorporating initial corrupted image information at each step.
To enable direct use of pretrained I2SB models without retraining by ensuring marginal distribution consistency.

Main Methods:

Developed I3SB, a non-Markovian generative framework that integrates the initial corrupted image throughout the process.
Ensured marginal distribution consistency to allow seamless integration with pretrained I2SB models.
Conducted extensive experiments across diverse image corruptions (noise, low resolution, JPEG, sparse sampling) and modalities (natural, face, medical images).

Main Results:

I3SB significantly accelerates the generative process compared to I2SB.
Achieved equivalent perceptual quality with fewer generative steps.
Maintained or improved fidelity to the ground truth across various image restoration tasks.
Demonstrated effectiveness on natural, human face, and medical image datasets.

Conclusions:

I3SB offers a substantial speed improvement for image restoration tasks over existing I2SB methods.
The non-Markovian approach effectively preserves and utilizes corrupted image information for faster generation.
I3SB provides a practical and efficient solution for high-quality image restoration without compromising fidelity.