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图像处理中的深度学习，主要是原理的讲解，比较易懂,对于。-Image processing in deep learning, mainly to explain the principle, relatively easy to understand.
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通过波尔兹曼机深度学习进行图像分割,分割效果明显好于一般方法，并且通用性较强。-Boltzmann machine through deep learning for image segmentation
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介绍了用深度学习方法做超分辨率缩放问题，都要是2014年ECCV的两篇论文-deep learning super-resolution eccv2014 paper
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基于深度学习的图像超分辨率算法。
参考论文 Learning a Deep Convolutional Network for Image Super-Resolution (ECCV 2014)
-Super resolution based on deep learning
Refer to Learning a Deep Convolutional Network for Image Super-Resolution (ECCV 2014)
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orl人脸数据库用到深度学习工具箱之前的预处理，即将图像表达成字符串的形式。并将数据库一半分为测试数据，另一半训练数据。给出orl-Pretreat Orl face before using it in deep learning tool box, in the form of the image expressed by the string. Half are divided into test data, and the other half are training data.
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cvpr2016论文Deep Learning of Binary Hash Codes for Fast Image Retri (HDS)对应的matlab源代码-cvpr2016 paper Deep Learning of Binary Hash Codes for Fast Image Retri (HDS) corresponding matlab source code
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基于区域图和深度相似性表征的SAR图像分割，关于深度学习的一个文章，很有代表性，可以参考。-SAR image area map and characterize the depth of similarity based segmentation on a deep learning of the article, very representative, you can refer to.
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通过CNN计算大气散射模型中的传播率，从而实现图像去雾-multi-scale deep neural network for single-image dehazing
by learning the mapping between hazy images and their corresponding
transmission maps
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主要用于图像增强，眼白血管纹理增强，运用的是深度学习算法，超分辨率(For image enhancement, white vascular texture enhancement is the use of deep learning algorithms, super-resolution)
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这是一个基于深度学习的超分辨率图像复原技术的例子(This is a deep learning based super-resolution image restoration technology for example)
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We develop a new edge detection algorithm, holistically-nested edge detection (HED), which performs image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets. HED automat
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LINUX环境，对图像中物体打标签，深度学习。(In LINUX,tagging labels of the object in the image, deep learning.)
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Recent innovations in training deep convolutional neural network models have motivated the design of new methods to automatically learn local image descriptors. The latest deep ConvNets proposed for this task consist(from machine learning show that
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图像处理python表代码。其中有一部分是基于深度学习的。(Image processing Python table code. Some of them are based on deep learning.)
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