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We present a method that takes as input a single dual-pixel image, and simultaneously estimates the image's defocus map---the amount of defocus blur at each pixel---and recovers an all-in-focus image. Our method is inspired from recent works that leverage the dual-pixel sensors available in many consumer cameras to assist with autofocus, and use them for recovery of defocus maps or all-in-focus images. These prior works have solved the two recovery problems independently of each other, and often require large labeled datasets for supervised training. By contrast, we show that it is beneficial to treat these two closely-connected problems simultaneously. To this end, we set up an optimization problem that, by carefully modeling the optics of dual-pixel images, jointly solves both problems. We use data captured with a consumer smartphone camera to demonstrate that, after a one-time calibration step, our approach improves upon prior works for both defocus map estimation and blur removal, despite being entirely unsupervised.
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Self-Supervised Single-Image Depth Estimation From Focus and Defocus Clues
Self-supervised depth estimation has recently demonstrated promising performance compared to the supervised methods on challenging indoor scenes. However, the majority of efforts mainly focus on exploiting photometric and geometric consistency via forward image warping and backward image warping, based on monocular videos or stereo image pairs. The influence of defocus blur to depth estimation is neglected, resulting in a limited performance for objects and scenes in out of focus. In this work, we propose the first framework for simultaneous depth estimation from a single image and image focal stacks using depth-from-defocus and depth-from-focus algorithms. The proposed network is able to learn optimal depth mapping from the information contained in the blur of a single image, generate a simulated image focal stack and all-in-focus image, and train a depth estimator from an image focal stack. In addition to the validation of our method on both synthetic NYUv2 dataset and real DSLR dataset, we also collect our own dataset using a DSLR camera and further verify on it. Experiments demonstrate that our system surpasses the state-of-the-art supervised depth estimation method over 4% in accuracy and achieves superb performance among the methods without direct supervision on the synthesized NYUv2 dataset, which has been rarely explored.
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- PAR ID:
- 10426985
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE robotics automation letters
- ISSN:
- 2377-3766
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/LRA.2021.3092258
