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INNFusion: A Diffusion-Based Blind Image Super Resolution Scheme Using Reversible Degradation Process With Invertible

Morteza Poudineh, Alireza Esmaeilzehi, M Omair Ahmad

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|April 24, 2026

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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.

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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.