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METHODS AND SYSTEMS USING IMPROVED CONVOLUTIONAL NEURAL NETWORKS FOR IMAGES PROCESSING

机译:使用改进的卷积神经网络进行图像处理的方法和系统

摘要

Methods and systems are disclosed using improved Convolutional Neural Networks (CNN) for image processing. In one example, an input image is down-sampled into smaller images with a smaller resolution than the input image. The down-sampled smaller images are processed by a CNN having a last layer with a reduced number of nodes than a last layer of a full CNN used to process the input image at a full resolution. A result is outputted based on the processed down-sampled smaller images by the CNN having a last layer with a reduced number of nodes. In another example, shallow CNN networks are built randomly. The randomly built shallow CNN networks are combined to imitate a trained deep neural network (DNN).
机译:公开了使用改进的卷积神经网络(CNN)进行图像处理的方法和系统。在一个示例中,输入图像被下采样为分辨率比输入图像小的图像。下采样的较小图像由CNN处理,该CNN的最后一层的节点数少于用于以全分辨率处理输入图像的完整CNN的最后一层的节点数。通过具有减少的节点数的最后一层的CNN,基于处理后的降采样后的较小图像输出结果。在另一个示例中,浅层CNN网络是随机构建的。随机构建的浅层CNN网络被组合以模仿训练有素的深度神经网络(DNN)。

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