Adjust Your Focus: Defocus Deblurring From Dual-Pixel Images Using Explicit Multi-Scale Cross-Correlation
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arXiv
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| Natura: | Preprint |
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2025
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| _version_ | 1866910830846541824 |
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| author | Swami, Kunal |
| author_facet | Swami, Kunal |
| contents | Defocus blur is a common problem in photography. It arises when an image is captured with a wide aperture, resulting in a shallow depth of field. Sometimes it is desired, e.g., in portrait effect. Otherwise, it is a problem from both an aesthetic point of view and downstream computer vision tasks, such as segmentation and depth estimation. Defocusing an out-of-focus image to obtain an all-in-focus image is a highly challenging and often ill-posed problem. A recent work exploited dual-pixel (DP) image information, widely available in consumer DSLRs and high-end smartphones, to solve the problem of defocus deblurring. DP sensors result in two sub-aperture views containing defocus disparity cues. A given pixel's disparity is directly proportional to the distance from the focal plane. However, the existing methods adopt a naïve approach of a channel-wise concatenation of the two DP views without explicitly utilizing the disparity cues within the network. In this work, we propose to perform an explicit cross-correlation between the two DP views to guide the network for appropriate deblurring in different image regions. We adopt multi-scale cross-correlation to handle blur and disparities at different scales. Quantitative and qualitative evaluation of our multi-scale cross-correlation network (MCCNet) reveals that it achieves better defocus deblurring than existing state-of-the-art methods despite having lesser computational complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11002 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adjust Your Focus: Defocus Deblurring From Dual-Pixel Images Using Explicit Multi-Scale Cross-Correlation Swami, Kunal Computer Vision and Pattern Recognition Defocus blur is a common problem in photography. It arises when an image is captured with a wide aperture, resulting in a shallow depth of field. Sometimes it is desired, e.g., in portrait effect. Otherwise, it is a problem from both an aesthetic point of view and downstream computer vision tasks, such as segmentation and depth estimation. Defocusing an out-of-focus image to obtain an all-in-focus image is a highly challenging and often ill-posed problem. A recent work exploited dual-pixel (DP) image information, widely available in consumer DSLRs and high-end smartphones, to solve the problem of defocus deblurring. DP sensors result in two sub-aperture views containing defocus disparity cues. A given pixel's disparity is directly proportional to the distance from the focal plane. However, the existing methods adopt a naïve approach of a channel-wise concatenation of the two DP views without explicitly utilizing the disparity cues within the network. In this work, we propose to perform an explicit cross-correlation between the two DP views to guide the network for appropriate deblurring in different image regions. We adopt multi-scale cross-correlation to handle blur and disparities at different scales. Quantitative and qualitative evaluation of our multi-scale cross-correlation network (MCCNet) reveals that it achieves better defocus deblurring than existing state-of-the-art methods despite having lesser computational complexity. |
| title | Adjust Your Focus: Defocus Deblurring From Dual-Pixel Images Using Explicit Multi-Scale Cross-Correlation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.11002 |