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INNFusion: A Diffusion-Based Blind Image Super Resolution Scheme Using Reversible Degradation Process With Invertible
Summary
This study introduces a new diffusion-based method for blind image super-resolution using invertible neural networks. This approach enhances image quality and outperforms existing state-of-the-art techniques.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Deep neural networks with generative diffusion priors achieve state-of-the-art blind image super-resolution.
- These methods generate high-quality images but suffer from large parameter counts and difficult training.
Purpose of the Study:
- To propose a novel diffusion-based blind image super-resolution scheme.
- To address the limitations of current deep learning models in terms of training difficulty and performance.
Main Methods:
- Utilizing invertible neural networks (INNs) within a diffusion-based framework.
- Leveraging the reversibility property of INNs to generate degraded images representing the upper bound of the super-resolution function space.
- Incorporating these generated images into the training process.
Main Results:
- The proposed scheme facilitates the learning paradigm for blind image super-resolution.
- Achieved superior performance compared to existing state-of-the-art methods.
- Demonstrated enhanced image quality with realistic textures and structures.
Conclusions:
- The novel learning algorithm with invertible neural networks offers a more effective approach to blind image super-resolution.
- This method overcomes training challenges and improves performance.
- The technique sets a new benchmark for image super-resolution tasks.
