예제 #1
0
def mixingsubband(fimau,fimao):
    '''
    References
    ----------
    Pierrick Coupe - [email protected]                                  
    Jose V. Manjon - [email protected]                                        
    Brain Imaging Center, Montreal Neurological Institute.                     
    Mc Gill University                                                         
    Copyright (C) 2010 Pierrick Coupe and Jose V. Manjon                       
    '''
    s=fimau.shape
    p0 = 2**(np.ceil(np.log2(s[0])))
    p1 = 2**(np.ceil(np.log2(s[1])))
    p2 = 2**(np.ceil(np.log2(s[2])))
    pad1 = np.zeros((p0,p1,p2));
    pad2 = pad1.copy();
    pad1[:s[0],:s[1],:s[2]] = fimau[...]
    pad2[:s[0],:s[1],:s[2]] = fimao[...]
    af = np.array([  [0, -0.01122679215254],
            [0, 0.01122679215254],
            [-0.08838834764832,   0.08838834764832],
            [0.08838834764832,   0.08838834764832],
            [0.69587998903400,  -0.69587998903400],
            [0.69587998903400,   0.69587998903400],
            [0.08838834764832,  -0.08838834764832],
            [-0.08838834764832,  -0.08838834764832],
            [0.01122679215254,                  0],
            [0.01122679215254,                  0]])
    sf=np.array(af[::-1,:])
    w1 = dwt3D(pad1,1,af)
    w2 = dwt3D(pad2,1,af)
    w1[0][2] = w2[0][2]
    w1[0][4] = w2[0][4]
    w1[0][5] = w2[0][5]
    w1[0][6] = w2[0][6]
    fima = idwt3D(w1,1,sf)
    fima = fima[:s[0],:s[1],:s[2]];
    # TO-DO: NAN checking
    #ind=np.isnan(fima)
    #fima[ind]=fimau[ind];
    # negative checking (only for rician noise mixing)
    fima[fima<0]=0
    return fima
예제 #2
0
def ascm(ima,fimau,fimao,h):
    '''
    Adaptive Soft (wavelet) Coefficient Mixing proposed by P. Coupe et al.
    Combines two filtered 3D-images at different resolutions and the orginal
    image. Returns the resulting combined image.
    Parameters
    ----------
        ima: the original (not filtered) image
        fimau : 3D double array,
            filtered image with optimized non-local means using a small block 
            (suggested:3x3), which corresponds to a "high resolution" filter.
        fimao : 3D double array,
            filtered image with optimized non-local means using a small block 
            (suggested:5x5), which corresponds to a "low resolution" filter.
        h: the estimated standard deviation of the Gaussian random variables
            that explain the rician noise. Note: In P. Coupe et al. the 
            rician noise was simulated as sqrt((f+x)^2 + (y)^2) where f is 
            the pixel value and x and y are independent realizations of a 
            random variable with Normal distribution, with mean=0 and 
            standard deviation=h
    References
    ----------
    Pierrick Coupe - [email protected]                                  
    Jose V. Manjon - [email protected]                                        
    Brain Imaging Center, Montreal Neurological Institute.                     
    Mc Gill University                                                         
                                                                               
    Copyright (C) 2008 Pierrick Coupe and Jose V. Manjon                       

    ************************************************************************
    *              3D Adaptive Multiresolution Non-Local Means Filter      *
    *           P. Coupe a, J. V. Manjon, M. Robles , D. L. Collin         * 
    ************************************************************************
    '''
    s=fimau.shape;
    p=[0,0,0]
    p[0]=2**math.ceil(math.log(s[0],2))
    p[1]=2**math.ceil(math.log(s[1],2))
    p[2]=2**math.ceil(math.log(s[2],2))
    pad1=np.zeros((p[0],p[1],p[2]));
    pad2=np.zeros((p[0],p[1],p[2]));
    pad3=np.zeros((p[0],p[1],p[2]));
    pad1[:s[0], :s[1], :s[2]]=fimau[:,:,:]
    pad2[:s[0], :s[1], :s[2]]=fimao[:,:,:]
    pad3[:s[0], :s[1], :s[2]]=ima[:,:,:]
    af = np.array([  [0, -0.01122679215254],
            [0, 0.01122679215254],
            [-0.08838834764832,   0.08838834764832],
            [0.08838834764832,   0.08838834764832],
            [0.69587998903400,  -0.69587998903400],
            [0.69587998903400,   0.69587998903400],
            [0.08838834764832,  -0.08838834764832],
            [-0.08838834764832,  -0.08838834764832],
            [0.01122679215254,                  0],
            [0.01122679215254,                  0]])
    sf=np.array(af[::-1,:])
    w1= dwt3D.dwt3D(pad1,1,af)
    w2= dwt3D.dwt3D(pad2,1,af)
    w3= dwt3D.dwt3D(pad3,1,af)
    for i in xrange(7):
        tmp = np.array(w3[0][i])
        tmp = tmp[:(s[0]//2), :(s[1]//2), :(s[2]//2)]
        sigY = np.std(tmp, ddof=1)
        sigX = (sigY*sigY) - h*h
        if sigX<0:
            T=abs(w3[0][i]).max()
        else:
            T=(h*h)/(sigX**0.5)
        w3[0][i]=abs(w3[0][i])
        dist=np.array(w3[0][i])-T
        dist=np.exp(-0.01*dist)
        dist=1./(1+dist)
        w3[0][i]=dist*w1[0][i] + (1-dist)*w2[0][i]
    w3[1]=w1[1]
    fima= idwt3D.idwt3D(w3,1,sf)
    fima=fima[:s[0], :s[1], :s[2]]
    return fima
예제 #3
0
def hsm(fimau, fimao):
    '''
    Hard Subband Mixing algorithm, proposed by P. Coupe et al.
    Combines two filtered 3D-images at different resolutions. Returns the
    resulting combined image.
    Parameters
    ----------
        fimau : 3D double array,
            filtered image with optimized non-local means using a small block 
            (suggested:3x3), which corresponds to a "high resolution" filter.
        fimao : 3D double array,
            filtered image with optimized non-local means using a small block 
            (suggested:5x5), which corresponds to a "low resolution" filter.
    References
    ----------
    Pierrick Coupe - [email protected]                                  
    Jose V. Manjon - [email protected]                                        
    Brain Imaging Center, Montreal Neurological Institute.                     
    Mc Gill University                                                         
    Copyright (C) 2008 Pierrick Coupe and Jose V. Manjon                       
    ************************************************************************
    *              3D Adaptive Multiresolution Non-Local Means Filter      *
    *            P. Coupe a, J. V. Manjon, M. Robles , D. L. Collin        * 
    ************************************************************************
    
                              Details on Wavelet mixing                         
    ************************************************************************
    *  The hard wavelet subbands mixing is described in:                   *
    *                                                                      *
    *  P. Coupe, S. Prima, P. Hellier, C. Kervrann, C. Barillot.           *
    *  3D Wavelet Sub-Bands Mixing for Image Denoising                     *
    *  International Journal of Biomedical Imaging, 2008                   * 
    ************************************************************************
    '''
    s = fimau.shape
    p = [0, 0, 0]
    p[0] = 2**math.ceil(math.log(s[0], 2))
    p[1] = 2**math.ceil(math.log(s[1], 2))
    p[2] = 2**math.ceil(math.log(s[2], 2))
    pad1 = np.zeros((p[0], p[1], p[2]))
    pad2 = np.zeros((p[0], p[1], p[2]))
    pad1[:s[0], :s[1], :s[2]] = fimau
    pad2[:s[0], :s[1], :s[2]] = fimao
    af = np.array([[0, -0.01122679215254], [0, 0.01122679215254],
                   [-0.08838834764832, 0.08838834764832],
                   [0.08838834764832, 0.08838834764832],
                   [0.69587998903400, -0.69587998903400],
                   [0.69587998903400, 0.69587998903400],
                   [0.08838834764832, -0.08838834764832],
                   [-0.08838834764832, -0.08838834764832],
                   [0.01122679215254, 0], [0.01122679215254, 0]])
    sf = np.array(af[::-1, :])
    w1 = dwt3D.dwt3D(pad1, 1, af)
    w2 = dwt3D.dwt3D(pad2, 1, af)
    #w1[0][2] = (w2[0][2]+w1[0][2])/2;
    #w1[0][4] = (w2[0][4]+w1[0][4])/2;
    #w1[0][5] = (w2[0][5]+w1[0][5])/2;
    #w1[0][6] = (w2[0][6]+w1[0][6])/2;
    w1[0][2] = w2[0][2]
    w1[0][4] = w2[0][4]
    w1[0][5] = w2[0][5]
    w1[0][6] = w2[0][6]
    fima = idwt3D.idwt3D(w1, 1, sf)
    fima = fima[:s[0], :s[1], :s[2]]
    return fima
예제 #4
0
def ascm(ima, fimau, fimao, h):
    '''
    Adaptive Soft (wavelet) Coefficient Mixing proposed by P. Coupe et al.
    Combines two filtered 3D-images at different resolutions and the orginal
    image. Returns the resulting combined image.
    Parameters
    ----------
        ima: the original (not filtered) image
        fimau : 3D double array,
            filtered image with optimized non-local means using a small block 
            (suggested:3x3), which corresponds to a "high resolution" filter.
        fimao : 3D double array,
            filtered image with optimized non-local means using a small block 
            (suggested:5x5), which corresponds to a "low resolution" filter.
        h: the estimated standard deviation of the Gaussian random variables
            that explain the rician noise. Note: In P. Coupe et al. the 
            rician noise was simulated as sqrt((f+x)^2 + (y)^2) where f is 
            the pixel value and x and y are independent realizations of a 
            random variable with Normal distribution, with mean=0 and 
            standard deviation=h
    References
    ----------
    Pierrick Coupe - [email protected]                                  
    Jose V. Manjon - [email protected]                                        
    Brain Imaging Center, Montreal Neurological Institute.                     
    Mc Gill University                                                         
                                                                               
    Copyright (C) 2008 Pierrick Coupe and Jose V. Manjon                       

    ************************************************************************
    *              3D Adaptive Multiresolution Non-Local Means Filter      *
    *           P. Coupe a, J. V. Manjon, M. Robles , D. L. Collin         * 
    ************************************************************************
    '''
    s = fimau.shape
    p = [0, 0, 0]
    p[0] = 2**math.ceil(math.log(s[0], 2))
    p[1] = 2**math.ceil(math.log(s[1], 2))
    p[2] = 2**math.ceil(math.log(s[2], 2))
    pad1 = np.zeros((p[0], p[1], p[2]))
    pad2 = np.zeros((p[0], p[1], p[2]))
    pad3 = np.zeros((p[0], p[1], p[2]))
    pad1[:s[0], :s[1], :s[2]] = fimau[:, :, :]
    pad2[:s[0], :s[1], :s[2]] = fimao[:, :, :]
    pad3[:s[0], :s[1], :s[2]] = ima[:, :, :]
    af = np.array([[0, -0.01122679215254], [0, 0.01122679215254],
                   [-0.08838834764832, 0.08838834764832],
                   [0.08838834764832, 0.08838834764832],
                   [0.69587998903400, -0.69587998903400],
                   [0.69587998903400, 0.69587998903400],
                   [0.08838834764832, -0.08838834764832],
                   [-0.08838834764832, -0.08838834764832],
                   [0.01122679215254, 0], [0.01122679215254, 0]])
    sf = np.array(af[::-1, :])
    w1 = dwt3D.dwt3D(pad1, 1, af)
    w2 = dwt3D.dwt3D(pad2, 1, af)
    w3 = dwt3D.dwt3D(pad3, 1, af)
    for i in xrange(7):
        tmp = np.array(w3[0][i])
        tmp = tmp[:(s[0] // 2), :(s[1] // 2), :(s[2] // 2)]
        sigY = np.std(tmp, ddof=1)
        sigX = (sigY * sigY) - h * h
        if sigX < 0:
            T = abs(w3[0][i]).max()
        else:
            T = (h * h) / (sigX**0.5)
        w3[0][i] = abs(w3[0][i])
        dist = np.array(w3[0][i]) - T
        dist = np.exp(-0.01 * dist)
        dist = 1. / (1 + dist)
        w3[0][i] = dist * w1[0][i] + (1 - dist) * w2[0][i]
    w3[1] = w1[1]
    fima = idwt3D.idwt3D(w3, 1, sf)
    fima = fima[:s[0], :s[1], :s[2]]
    return fima
예제 #5
0
파일: hsm.py 프로젝트: omarocegueda/denoise
def hsm(fimau, fimao):
    '''
    Hard Subband Mixing algorithm, proposed by P. Coupe et al.
    Combines two filtered 3D-images at different resolutions. Returns the
    resulting combined image.
    Parameters
    ----------
        fimau : 3D double array,
            filtered image with optimized non-local means using a small block 
            (suggested:3x3), which corresponds to a "high resolution" filter.
        fimao : 3D double array,
            filtered image with optimized non-local means using a small block 
            (suggested:5x5), which corresponds to a "low resolution" filter.
    References
    ----------
    Pierrick Coupe - [email protected]                                  
    Jose V. Manjon - [email protected]                                        
    Brain Imaging Center, Montreal Neurological Institute.                     
    Mc Gill University                                                         
    Copyright (C) 2008 Pierrick Coupe and Jose V. Manjon                       
    ************************************************************************
    *              3D Adaptive Multiresolution Non-Local Means Filter      *
    *            P. Coupe a, J. V. Manjon, M. Robles , D. L. Collin        * 
    ************************************************************************
    
                              Details on Wavelet mixing                         
    ************************************************************************
    *  The hard wavelet subbands mixing is described in:                   *
    *                                                                      *
    *  P. Coupe, S. Prima, P. Hellier, C. Kervrann, C. Barillot.           *
    *  3D Wavelet Sub-Bands Mixing for Image Denoising                     *
    *  International Journal of Biomedical Imaging, 2008                   * 
    ************************************************************************
    '''
    s=fimau.shape;
    p=[0,0,0]
    p[0]=2**math.ceil(math.log(s[0],2))
    p[1]=2**math.ceil(math.log(s[1],2))
    p[2]=2**math.ceil(math.log(s[2],2))
    pad1=np.zeros((p[0],p[1],p[2]));
    pad2=np.zeros((p[0],p[1],p[2]));
    pad1[:s[0], :s[1], :s[2]]=fimau;
    pad2[:s[0], :s[1], :s[2]]=fimao;
    af = np.array([  [0, -0.01122679215254],
            [0, 0.01122679215254],
            [-0.08838834764832,   0.08838834764832],
            [0.08838834764832,   0.08838834764832],
            [0.69587998903400,  -0.69587998903400],
            [0.69587998903400,   0.69587998903400],
            [0.08838834764832,  -0.08838834764832],
            [-0.08838834764832,  -0.08838834764832],
            [0.01122679215254,                  0],
            [0.01122679215254,                  0]]);
    sf=np.array(af[::-1,:])
    w1=dwt3D.dwt3D(pad1,1,af);
    w2=dwt3D.dwt3D(pad2,1,af);
    #w1[0][2] = (w2[0][2]+w1[0][2])/2;
    #w1[0][4] = (w2[0][4]+w1[0][4])/2;
    #w1[0][5] = (w2[0][5]+w1[0][5])/2;
    #w1[0][6] = (w2[0][6]+w1[0][6])/2;
    w1[0][2] = w2[0][2]
    w1[0][4] = w2[0][4]
    w1[0][5] = w2[0][5]
    w1[0][6] = w2[0][6]
    fima = idwt3D.idwt3D(w1,1,sf);
    fima = fima[:s[0],:s[1],:s[2]];
    return fima