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Image deblurring is a foundational problem with numerous application, and the face deblurring subject is one of the most interesting branches. We propose a convolutional neural network (CNN)-based architecture that embraces multi-scale deep features. In this paper, we address the deblurring problems with transfer learning via a multi-task embedding network; the proposed method is effective at restoring...
Traditional convolutional neural networks (CNNs) classify all categories by a single network, which passes all kinds of samples through totally the same network flow. In fact, it is quite challengeable to distinguish schooner with ketch and chair by a single network. To address it, we propose a new image classification architecture composed of a cluster algorithm and the Tree-CNN. The cluster algorithm...
Correlation filter-based trackers have recently shown excellent performance in terms of motion blur and illumination changes, but they are notoriously sensitive to deformation. It has been demonstrated that the combination of the correlation filter-based tracker and the color histogram-based tracker can alleviate the deformation and keep advantages of the correlation filter-based tracker. However,...
In this paper, we propose a novel image enhancement algorithm via anti-degraded model and L1L2-based variational retinex (AD-L1L2VR) for non-uniform illumination endoscopic images. Firstly, a haze-free endoscopic image is obtained by an anti-degraded model named dark channel prior (DCP). For getting a more accurate transmission map, it is refined by using a guided image filtering. Secondly, the haze-free...
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