A Novel Method and Dataset for Depth-Guided Image Deblurring From Smartphone Lidar
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| Title: | A Novel Method and Dataset for Depth-Guided Image Deblurring From Smartphone Lidar |
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| Authors: | Montanaro, Antonio, Valsesia, Diego |
| Source: | 2025 IEEE International Conference on Image Processing (ICIP). :175-180 |
| Publication Status: | Preprint |
| Publisher Information: | IEEE, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Image deblurring, Depth maps, Lidar, Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Image and Video Processing |
| Description: | Modern smartphones are equipped with Lidar sensors providing depth-sensing capabilities. Recent works have shown that this complementary sensor allows to improve various tasks in image processing, including deblurring. However, there is a current lack of datasets with realistic blurred images and paired mobile Lidar depth maps to further study the topic. At the same time, there is also a lack of blind zero-shot methods that can deblur a real image using the depth guidance without requiring extensive training sets of paired data. In this paper, we propose an image deblurring method based on denoising diffusion models that can leverage the Lidar depth guidance and does not require training data with paired Lidar depth maps. We also present the first dataset with real blurred images with corresponding Lidar depth maps and sharp ground truth images, acquired with an Apple iPhone 15 Pro, for the purpose of studying Lidar-guided deblurring. Experimental results on this novel dataset show that Lidar guidance is effective and the proposed method outperforms state-of-the-art deblurring methods in terms of perceptual quality. |
| Document Type: | Article Conference object |
| File Description: | application/pdf |
| DOI: | 10.1109/icip55913.2025.11084288 |
| DOI: | 10.48550/arxiv.2509.09241 |
| Access URL: | http://arxiv.org/abs/2509.09241 |
| Rights: | STM Policy #29 CC BY |
| Accession Number: | edsair.doi.dedup.....6e9c8c41941f4ead0d5993aa2aebfdca |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Modern smartphones are equipped with Lidar sensors providing depth-sensing capabilities. Recent works have shown that this complementary sensor allows to improve various tasks in image processing, including deblurring. However, there is a current lack of datasets with realistic blurred images and paired mobile Lidar depth maps to further study the topic. At the same time, there is also a lack of blind zero-shot methods that can deblur a real image using the depth guidance without requiring extensive training sets of paired data. In this paper, we propose an image deblurring method based on denoising diffusion models that can leverage the Lidar depth guidance and does not require training data with paired Lidar depth maps. We also present the first dataset with real blurred images with corresponding Lidar depth maps and sharp ground truth images, acquired with an Apple iPhone 15 Pro, for the purpose of studying Lidar-guided deblurring. Experimental results on this novel dataset show that Lidar guidance is effective and the proposed method outperforms state-of-the-art deblurring methods in terms of perceptual quality. |
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| DOI: | 10.1109/icip55913.2025.11084288 |