神经网络与十大星系Neural Networks and GREAT10 Galaxies |
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课程网址: | http://videolectures.net/nipsworkshops2011_gauci_neural/ |
主讲教师: | Adam Gauci |
开课单位: | 马耳他大学 |
开课时间: | 2012-06-23 |
课程语种: | 英语 |
中文简介: | 这项工作研究人工神经网络(ANNs)在去模糊星系明信片的GREAT10挑战。建立高分辨率模型,用给定的点扩散函数(PSF)卷积得到相应的模糊图像。然后在傅里叶空间中进行下采样以获得挑战中使用的分辨率。人工神经网络的训练实例是从原始明信片和模糊明信片中创建的。将模糊图像中的nxn(对于某些奇数n)窗口与原始图像中的同一窗口进行比较,神经网络学习输出正确的中间像素强度。这意味着在输入向量中使用相邻像素的强度。研究了将神经网络输出向量转换为像素值的不同加权方案。研究了不同像素大小的人工神经网络编码方法的优点。用去模糊图像与原始模型之间的卡方误差来衡量性能。 |
课程简介: | This work investigates the application of artificial neural networks (ANNs) to deblur galaxy postcards of the GREAT10 challenge. High resolution models are created and convolved with a given Point Spread Function (PSF) to generate the corresponding blurred images. These are then downsampled in Fourier space to obtain the resolution used in the challenge. Training examples for the ANN are created from original and the blurred postcards. An n X n, for some odd n, window in a blurred image is compared to the same window in the original images and the ANN learns to output the correct intensity of the middle pixel. This means that the intensities of neighbouring pixels are used in the input vector. Different weightings schemes for translating the output vector from the ANN into pixel values are investigated. The advantages gained by using different window sizes, pixel encoding methods, and the number of hidden neurons in the ANN are also researched. The chi-squared error between the deblurred image and the original model is used to measure the performance. |
关 键 词: | 十大星系; 人工神经网络; 模糊图像 |
课程来源: | 视频讲座网 |
数据采集: | 2020-11-30:yxd |
最后编审: | 2021-09-20:zyk |
阅读次数: | 70 |