Note
This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.
7.5.17. nilearn.image.smooth_img¶

nilearn.image.
smooth_img
(imgs, fwhm)¶ Smooth images by applying a Gaussian filter.
Apply a Gaussian filter along the three first dimensions of arr. In all cases, nonfinite values in input image are replaced by zeros.
Parameters: imgs: Niimglike object or iterable of Niimglike objects
See http://nilearn.github.io/manipulating_images/input_output.html Image(s) to smooth.
fwhm: scalar, numpy.ndarray, ‘fast’ or None
Smoothing strength, as a FullWidth at Half Maximum, in millimeters. If a scalar is given, width is identical on all three directions. A numpy.ndarray must have 3 elements, giving the FWHM along each axis. If fwhm == ‘fast’, a fast smoothing will be performed with a filter [0.2, 1, 0.2] in each direction and a normalisation to preserve the scale. If fwhm is None, no filtering is performed (useful when just removal of nonfinite values is needed).
In corner case situations, fwhm is simply kept to None when fwhm is specified as fwhm=0.
Returns: filtered_img: nibabel.Nifti1Image or list of.
Input image, filtered. If imgs is an iterable, then filtered_img is a list.