MATLAB給圖像添加高斯、椒鹽、加性及乘性噪聲[噪聲生成]源代碼Gaussian-Pepper-Noise-generator
標(biāo)簽： Gaussian-Pepper-Noise-generator MATLAB 圖像 乘性噪聲
上傳時(shí)間： 2017-03-23
上傳用戶：dave520l
VC++ based Gaussian Noise Generator + Kalman Filter
標(biāo)簽： Generator Gaussian Filter Kalman
上傳時(shí)間： 2016-09-06
上傳用戶：極客
A hardware Gaussian noise generator for channel code evaluation和A Gaussian noise generator for hardware-based simulations兩篇關(guān)于高斯白噪聲產(chǎn)生及信道估計(jì)的經(jīng)典論文
標(biāo)簽： generator Gaussian noise evaluation
上傳時(shí)間： 2017-09-09
上傳用戶：壞壞的華仔
Random Number Generators（隨機(jī)數(shù)生成）包括gaussian random number generator、uniform random number generator、low-frequency hold generator、1/f noise generator等5種隨機(jī)信號(hào)生成的c源代碼
標(biāo)簽： generator random number Generators
上傳時(shí)間： 2014-12-07
上傳用戶：edisonfather
Compute Classical detection threshold for radar detection under additive Gaussian white noise criterion and specified false alarm probability.
標(biāo)簽： detection Classical threshold additive
上傳時(shí)間： 2013-12-09
上傳用戶：hwl453472107
Image enhancement in frequency domain using Fourier center frequency, Gaussian lowpass filter, Low pass filter, high pass filter. Image restoration using medean filter, weiner filter with noise generator such as Gaussian noise, Salt and Pepper noise
標(biāo)簽： frequency enhancement Gaussian Fourier
上傳時(shí)間： 2017-08-24
上傳用戶：xinzhch
Compute Classical CFAR binary detection threshold for radar detection under additive Gaussian white noise criterion and specified false alarm probability.
標(biāo)簽： detection Classical threshold additive
上傳時(shí)間： 2013-12-22
上傳用戶：希醬大魔王
noise image processing test gaussian salt pepper additive.非常容易的一個(gè)code
標(biāo)簽： processing gaussian additive pepper
上傳時(shí)間： 2016-08-29
上傳用戶：woshiayin
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
標(biāo)簽： instantaneous algorithm Bayesian Gaussian
上傳時(shí)間： 2013-12-19
上傳用戶：jjj0202
An adaptive Gaussian filter for noise reduction and edge detection
標(biāo)簽： detection reduction adaptive Gaussian
上傳時(shí)間： 2014-01-08
上傳用戶：lunshaomo
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