def inner_jittered(T, inset=10, rtol=1e-3, atol=0):
    """move a stack of jittered noisy images in the direction of aligned"""
    image0 = gauss_image()
    Image = [image0 + image_noise(image0, p=.05) for _ in T]
    T = [tform.inset(image0.shape, inset) for tform in T]
    TImage, J = zip(*[warp_image_gradient(tform, image, normalize=True)
                      for tform, image in zip(T, Image)])
    _, _, dParamv = inner_ialm(TImage, J, tol=1e-4)

    # does dParamv move towards alignment? check if stdev of
    # parameters decreased.
    before = np.array([t.paramv for t in T])
    beforeStd = np.std(before, 0)
    after = np.array([t.paramv + dparamv
                      for t, dparamv in zip(T, dParamv)])
    afterStd = np.std(after, 0)
    assert np.all(np.logical_or(afterStd < beforeStd,
                                np.isclose(after, before, rtol=rtol, atol=atol)))