神经网络和杰出的10星系Neural Networks and GREAT10 Galaxies |
|
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: | 2012-01-23 |
课程语种: | 英语 |
中文简介: | 本研究探讨人工神经网路(ANN)在「大挑战」的黛布银河明信片中的应用。建立了高分辨率模型,并与给定的点扩散函数(PSF)进行卷积,生成相应的模糊图像。然后在傅立叶空间中减小这些采样,以获得挑战中使用的分辨率。人工神经网络的训练示例是根据原始和模糊的明信片创建的。对于一些奇数n,模糊图像中的一个n x n窗口与原始图像中的同一个窗口进行比较,然后神经网络学习输出中间像素的正确强度。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矢量转换为像素值的不同加权方案。研究了不同窗口大小、像素编码方法和神经网络中隐藏神经元数目对神经网络性能的影响。利用消隐图像与原始模型之间的卡方误差来测量其性能。 |
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: | 人工神经网络; 高分辨率模型; 点扩散函数 |
课程来源: | 视频讲座网 |
最后编审: | 2020-06-02:毛岱琦(课程编辑志愿者) |
阅读次数: | 64 |