BluRef: Unsupervised Image Deblurring with Dense-Matching References

• URL: http://arxiv.org/abs/2603.14176v1
• Date: Sun, 15 Mar 2026 01:31:31 GMT
• Title: BluRef: Unsupervised Image Deblurring with Dense-Matching References
• Abstract summary: This paper introduces a novel unsupervised approach for image deblurring.<n>It uses unpaired blurred and sharp images of similar scenes to generate pseudo-ground truth data.<n>Thanks to the simplicity of the training data collection process, our approach does not rely on existing paired training data or pre-trained networks.
• Score: 26.3346017592721
• License: http://creativecommons.org/licenses/by/4.0/
• 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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