Abstract
We present a novel, to the best of our knowledge, patch-based approach for depth regression from defocus blur. Most state-of-the-art methods for depth from defocus (DFD) use a patch classification approach among a set of potential defocus blurs related to a depth, which induces errors due to the continuous variation of the depth. Here, we propose to adapt a simple classification model using a soft-assignment encoding of the true depth into a membership probability vector during training and a regression scale to predict intermediate depth values. Our method uses no blur model or scene model; it only requires a training dataset of image patches (either raw, gray scale, or RGB) and their corresponding depth label. We show that our method outperforms both classification and direct regression on simulated images from structured or natural texture datasets, and on raw real data having optical aberrations from an active DFD experiment.
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Data availability
Data underlying the results on natural texture presented in Section 4.A are available in the Describable
Texture Dataset in Ref. [25].
Experimental data underlying the results on structured binary pattern
presented in Section 4.B are not
publicly available but may be obtained from the authors upon reasonable
request.
25. M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi, “Describing textures in the wild,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2014), pp. 3606–3613.
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