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Deep networks have achieved excellent performance in learning representation from visual data. However, the supervised deep models like convolutional neural network require large quantities of labeled data, which are very expensive to obtain. To solve this problem, this paper proposes an unsupervised deep network, called the stacked convolutional denoising auto-encoders, which can map images to hierarchical...
Deep networks like the convolutional neural network and its variants usually learn hierarchical features from labeled images, which is very expensive to obtain. How can we find an unsupervised way to effectively extract deep and abstract features from images without annotations? Even from large qualities of images with noise? In this paper, we propose a robust deep neural network, named as stacked...
In this paper, we introduced a deep learning model: Convolutional neural network(CNN) from the field of natural image classification and restoration, to solve general quality improving tasks for remote sensing images, including super-resolution, denoising and haze removal. To take advantage of the content similarity among aerial images and the learning ability of deep learning models, we proposed...
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