3f36b6c07ecac99da35de25ec0632d99c286f9b4,dipy/denoise/gibbs.py,,_gibbs_removal_1d,#,60

Before Change

    if axis:
        xs = x.copy()
    else:
        xs = x.T.copy()

    // TV for shift zero (baseline)
    tvr, tvl = _image_tv(xs, axis=1, n_points=n_points)
    tvp = np.minimum(tvr, tvl)
    tvn = tvp.copy()

    // Find optimal shift for gibbs removal
    isp = xs.copy()
    isn = xs.copy()
    sp = np.zeros(xs.shape)
    sn = np.zeros(xs.shape)
    N = xs.shape[1]
    c = np.fft.fftshift(np.fft.fft2(xs))
    k = np.linspace(-N/2, N/2-1, num=N)
    k = (2.0j * np.pi * k) / N
    for s in ssamp:
        // Access positive shift for given s
        img_p = abs(np.fft.ifft2(np.fft.fftshift(c * np.exp(k*s))))
        tvsr, tvsl = _image_tv(img_p, axis=1, n_points=n_points)
        tvs_p = np.minimum(tvsr, tvsl)

        // Access negative shift for given s
        img_n = abs(np.fft.ifft2(np.fft.fftshift(c * np.exp(-k*s))))
        tvsr, tvsl = _image_tv(img_n, axis=1, n_points=n_points)
        tvs_n = np.minimum(tvsr, tvsl)

        // Update positive shift params
        isp[tvp > tvs_p] = img_p[tvp > tvs_p]
        sp[tvp > tvs_p] = s
        tvp[tvp > tvs_p] = tvs_p[tvp > tvs_p]

        // Update negative shift params
        isn[tvn > tvs_n] = img_n[tvn > tvs_n]
        sn[tvn > tvs_n] = s
        tvn[tvn > tvs_n] = tvs_n[tvn > tvs_n]

    // check non-zero sub-voxel shifts
    idx = np.nonzero(sp + sn)

    // use positive and negative optimal sub-voxel shifts to interpolate to
    // original grid points

After Change

    
    ssamp = np.linspace(0.02, 0.9, num=45)

    xs = x.copy() if axis else x.T.copy()

    // TV for shift zero (baseline)
    tvr, tvl = _image_tv(xs, axis=1, n_points=n_points)
    tvp = np.minimum(tvr, tvl)
    tvn = tvp.copy()

    // Find optimal shift for gibbs removal
    isp = xs.copy()
    isn = xs.copy()
    sp = np.zeros(xs.shape)
    sn = np.zeros(xs.shape)
    N = xs.shape[1]
    c = np.fft.fftshift(np.fft.fft2(xs))
    k = np.linspace(-N/2, N/2-1, num=N)
    k = (2.0j * np.pi * k) / N
    for s in ssamp:
        // Access positive shift for given s
        img_p = abs(np.fft.ifft2(np.fft.fftshift(c * np.exp(k*s))))
        tvsr, tvsl = _image_tv(img_p, axis=1, n_points=n_points)
        tvs_p = np.minimum(tvsr, tvsl)

        // Access negative shift for given s
        img_n = abs(np.fft.ifft2(np.fft.fftshift(c * np.exp(-k*s))))
        tvsr, tvsl = _image_tv(img_n, axis=1, n_points=n_points)
        tvs_n = np.minimum(tvsr, tvsl)

        // Update positive shift params
        isp[tvp > tvs_p] = img_p[tvp > tvs_p]
        sp[tvp > tvs_p] = s
        tvp[tvp > tvs_p] = tvs_p[tvp > tvs_p]

        // Update negative shift params
        isn[tvn > tvs_n] = img_n[tvn > tvs_n]
        sn[tvn > tvs_n] = s
        tvn[tvn > tvs_n] = tvs_n[tvn > tvs_n]

    // check non-zero sub-voxel shifts
    idx = np.nonzero(sp + sn)

    // use positive and negative optimal sub-voxel shifts to interpolate to
    // original grid points

Instances