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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.

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This paper proposes a weakly-and self-supervised deep convolutional neural network (WSSDCNN) for contentaware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outputs a retargeted image. Retargeting is performed through a shift map, which is a pixel-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting. As a result, discriminative parts in an image are preserved, while background regions are adjusted seamlessly. In the training phase, pairs of an image and its image-level annotation are used to compute content and structure losses. We demonstrate the effectiveness of our proposed method for a retargeting application with insightful analyses. AbstractThis paper proposes weakly-and self-supervised deep convolutional neural networks (WSSDCNN) for contentsaware image retargeting. Our network takes a source image and a target aspect ratio, and then directly produces a retargeted image. Retargeting is performed through a shift map which is a pixel-wise mapping from source to target grid. Our method implicitly learns an attention map, which leads to a context-aware shift map for warping As a result, discriminative parts in an image are preserved, while background regions are adjusted seamlessly. In training phase, pairs of an image and its image level annotation used for computation of content and structure losses. We demonstrate effectiveness of the proposed method for the retargeting application with insightful analyses.