Prox for OperatorCompositionFunction

Wrappers/Python/cil/optimisation/functions/OperatorCompositionFunction.py

@@ -77,3 +77,30 @@ def gradient(self, x, out=None):
         self.function.gradient(tmp, out=tmp)
         return self.operator.adjoint(tmp, out=out)
 
+    def proximal(self, x, tau, out=None):
+
+        if not self.operator.is_orthogonal():
+            raise ValueError("Semi-orthogonality is required for operator.")
+
+        tmp = self.operator.range_geometry().allocate()
+        self.operator.direct(x, out=tmp)
+
+        if out is None:
+            return x + (1./self.operator.orthogonal_scalar)*self.operator.adjoint(self.function.proximal(tmp, tau=tau*self.operator.orthogonal_scalar) - tmp)
+        else:
+            self.operator.adjoint(self.function.proximal(tmp, tau=tau*self.operator.orthogonal_scalar) - tmp, out=out)
+            x.sapyb(1., out, 1./(self.operator.orthogonal_scalar), out=out)
+
+
+
+
+
+
+
+
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+

Wrappers/Python/cil/optimisation/operators/WaveletOperator.py

@@ -154,6 +154,9 @@ def __init__(self, domain_geometry,
             raise AttributeError(
                 f"Size of the range geometry is {range_geometry.shape} but the size of the wavelet coefficient array must be {tuple(range_shape)}.")
 
+        # semi-orthogonality constant
+        self.orthogonal_scalar = kwargs.get('orthogonal_scalar', 1.0)
+
         super().__init__(domain_geometry=domain_geometry, range_geometry=range_geometry)
 
     def _shape2slice(self):