Query: python convolve gives bright spot in center of image

Relevant code:
def convolve_gaussian_2d(image, gaussian_kernel_1d):
    """Convolve 2d gaussian."""
    result = scipy.ndimage.filters.correlate1d(
        image, gaussian_kernel_1d, axis=0)
    result = scipy.ndimage.filters.correlate1d(
        result, gaussian_kernel_1d, axis=1)
    return result

Similar but not relevant code:
def smooth_image(image, sigma, sigma_in_physical_coordinates=True, FWHM=False, max_kernel_width=32):
    """
    Smooth an image

    ANTsR function: `smoothImage`

    Arguments
    ---------
    image   
        Image to smooth
    
    sigma   
        Smoothing factor. Can be scalar, in which case the same sigma is applied to each dimension, or a vector of length dim(inimage) to specify a unique smoothness for each dimension.
    
    sigma_in_physical_coordinates : boolean  
        If true, the smoothing factor is in millimeters; if false, it is in pixels.
    
    FWHM : boolean    
        If true, sigma is interpreted as the full-width-half-max (FWHM) of the filter, not the sigma of a Gaussian kernel.
    
    max_kernel_width : scalar    
        Maximum kernel width
    
    Returns
    -------
    ANTsImage
    
    Example
    -------
    >>> import ants
    >>> image = ants.image_read( ants.get_ants_data('r16'))
    >>> simage = ants.smooth_image(image, (1.2,1.5))
    """
    if image.components == 1:
        return _smooth_image_helper(image, sigma, sigma_in_physical_coordinates, FWHM, max_kernel_width)
    else:
        imagelist = utils.split_channels(image)
        newimages = []
        for image in imagelist:
            newimage = _smooth_image_helper(image, sigma, sigma_in_physical_coordinates, FWHM, max_kernel_width)
            newimages.append(newimage)
        return utils.merge_channels(newimages)

Similar but not relevant code:
def zoom_cv(x,z):
    """ Zoom the center of image x by a factor of z+1 while retaining the original image size and proportion. """
    if z==0: return x
    r,c,*_ = x.shape
    M = cv2.getRotationMatrix2D((c/2,r/2),0,z+1.)
    return cv2.warpAffine(x,M,(c,r))

Similar but not relevant code:
def zoomed_scaled_array_around_mask(self, mask, buffer=1):
        """Extract the 2D region of an array corresponding to the rectangle encompassing all unmasked values.

        This is used to extract and visualize only the region of an image that is used in an analysis.

        Parameters
        ----------
        mask : mask.Mask
            The mask around which the scaled array is extracted.
        buffer : int
            The buffer of pixels around the extraction.
        """
        return self.new_with_array(array=array_util.extracted_array_2d_from_array_2d_and_coordinates(
            array_2d=self,  y0=mask.zoom_region[0]-buffer, y1=mask.zoom_region[1]+buffer,
            x0=mask.zoom_region[2]-buffer, x1=mask.zoom_region[3]+buffer))

Similar but not relevant code:
def _sim_fill(r1, r2, imsize):
    """
        calculate the fill similarity over the image
    """
    bbsize = (
        (max(r1["max_x"], r2["max_x"]) - min(r1["min_x"], r2["min_x"]))
        * (max(r1["max_y"], r2["max_y"]) - min(r1["min_y"], r2["min_y"]))
    )
    return 1.0 - (bbsize - r1["size"] - r2["size"]) / imsize