Clip denoise_tv_bregman output to [-1, 1] or [0, 1]

skimage/restoration/_denoise.py

@@ -149,7 +149,11 @@ def denoise_tv_bregman(image, weight, max_iter=100, eps=1e-3, isotropic=True):
     .. [4] http://www.math.ucsb.edu/~cgarcia/UGProjects/BregmanAlgorithms_JacquelineBush.pdf
 
     """
-    return _denoise_tv_bregman(image, weight, max_iter, eps, isotropic)
+    image = np.ascontiguousarray(np.atleast_3d(img_as_float(image)))
+    u = _denoise_tv_bregman(image, weight, max_iter, eps, isotropic)
+    minvalue = -1 if np.any(image < 0) else 0
+    maxvalue = 1
+    return np.clip(u, minvalue, maxvalue, out=u)
 
 
 def _denoise_tv_chambolle_nd(im, weight=0.1, eps=2.e-4, n_iter_max=200):

skimage/restoration/_denoise_cy.pyx

@@ -3,7 +3,6 @@
 #cython: nonecheck=False
 #cython: wraparound=False
 
-cimport numpy as cnp
 import numpy as np
 from libc.math cimport exp, fabs, sqrt
 from libc.float cimport DBL_MAX
@@ -148,10 +147,8 @@ def _denoise_bilateral(image, Py_ssize_t win_size, sigma_range,
     return np.squeeze(np.asarray(out))
 
 
-def _denoise_tv_bregman(image, double weight, int max_iter, double eps,
-                       char isotropic):
-    image = np.atleast_3d(img_as_float(image))
-
+def _denoise_tv_bregman(double[:, :, ::1] image, double weight, int max_iter,
+                        double eps, char isotropic):
     cdef:
         Py_ssize_t rows = image.shape[0]
         Py_ssize_t cols = image.shape[1]
@@ -166,7 +163,6 @@ def _denoise_tv_bregman(image, double weight, int max_iter, double eps,
     u = np.zeros(shape_ext, dtype=np.double)
 
     cdef:
-        double[:, :, ::1] cimage = np.ascontiguousarray(image)
         double[:, :, ::1] cu = u
 
         double[:, :, ::1] dx = np.zeros(shape_ext, dtype=np.double)
@@ -219,7 +215,7 @@ def _denoise_tv_bregman(image, double weight, int max_iter, double eps,
                             + bx[r, c, k]
                             - by[r - 1, c, k]
                             + by[r, c, k]
-                        ) + weight * cimage[r - 1, c - 1, k]
+                        ) + weight * image[r - 1, c - 1, k]
                     ) / norm
                     cu[r, c, k] = unew