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arXiv · 2603.14176

BluRef: Unsupervised Image Deblurring with Dense-Matching References

Bang-Dang Pham·Anh Tran·Cuong Pham·Minh Hoai
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Abstract

This paper introduces a novel unsupervised approach for image deblurring that utilizes a simple process for training data collection, thereby enhancing the applicability and effectiveness of deblurring methods. Our technique does not require meticulously paired data of blurred and corresponding sharp images; instead, it uses unpaired blurred and sharp images of similar scenes to generate pseudo-ground truth data by leveraging a dense matching model to identify correspondences between a blurry image and reference sharp images. Thanks to the simplicity of the training data collection process, our approach does not rely on existing paired training data or pre-trained networks, making it more adaptable to various scenarios and suitable for networks of different sizes, including those designed for low-resource devices. We demonstrate that this novel approach achieves state-of-the-art performance, marking a significant advancement in the field of image deblurring.

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Bang-Dang Pham, Anh Tran, Cuong Pham, Minh Hoai. 2026-03-15. BluRef: Unsupervised Image Deblurring with Dense-Matching References. https://arxiv.org/abs/2603.14176

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