def denoise_tv_chambolle(im, weight=0.1, eps=0.0002, n_iter_max=200, multichannel=False):
 im_type = im.dtype
 if (not (im_type.kind == 'f')):
  im = img_as_float(im)
 if multichannel:
  out = np.zeros_like(im)
  for c in range(im.shape[(-1)]):
   out[..., c] = _denoise_tv_chambolle_nd(im[..., c], weight, eps, n_iter_max)
 else:
  out = _denoise_tv_chambolle_nd(im, weight, eps, n_iter_max)
 return out

def denoise_tv_chambolle ( im , weight = 0.1 , eps = 0.0002 , n_iter_max = 200 , multichannel = False ) : im_type = im . dtype if ( not ( im_type . kind == 'f' ) ) : im = img_as_float ( im ) if multichannel : out = np . zeros_like ( im ) for c in range ( im . shape [ ( - 1 ) ] ) : out [ ... , c ] = _denoise_tv_chambolle_nd ( im [ ... , c ] , weight , eps , n_iter_max ) else : out = _denoise_tv_chambolle_nd ( im , weight , eps , n_iter_max ) return out
perform total-variation denoising on n-dimensional images .