A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus
Estimation
Abstract
In this paper, we introduce robust and synergetic handcrafted features and a simple but efficient deep feature from
a convolutional neural network (CNN) architecture for defocus estimation. This paper systematically analyzes the effectiveness of different features, and shows how each feature can compensate for the weaknesses of other features
when they are concatenated. For a full defocus map estimation, we extract image patches on strong edges sparsely,
after which we use them for deep and hand-crafted feature
extraction. In order to reduce the degree of patch-scale dependency, we also propose a multi-scale patch extraction
strategy. A sparse defocus map is generated using a neural
network classifier followed by a probability-joint bilateral
filter. The final defocus map is obtained from the sparse defocus map with guidance from an edge-preserving filtered
input image. Experimental results show that our algorithm
is superior to state-of-the-art algorithms in terms of defocus
estimation. Our work can be used for applications such as
segmentation, blur magnification, all-in-focus image generation, and 3-D estimation