diff --git a/scico/functional/_tvnorm.py b/scico/functional/_tvnorm.py
index b48bb7dec..930b71e31 100644
--- a/scico/functional/_tvnorm.py
+++ b/scico/functional/_tvnorm.py
@@ -123,7 +123,7 @@ def _center(idx: int, ndims: int) -> Tuple:
         """
         return (0,) * idx + (1,) + (0,) * (ndims - idx - 1)
 
-    def _haar_operator(self, ndims, input_shape, input_dtype):
+    def _haar_operator(self, ndims: int, input_shape: Shape, input_dtype: DType) -> LinearOperator:
         """Construct single-level shift-invariant Haar transform."""
         h0 = self.h0.astype(input_dtype)
         h1 = self.h1.astype(input_dtype)
@@ -142,7 +142,9 @@ def _haar_operator(self, ndims, input_shape, input_dtype):
         # single-level shift-invariant Haar transform
         return VerticalStack([L, H], jit=True)
 
-    def _prox_operators(self, ndims, input_shape, input_dtype):
+    def _prox_operators(
+        self, ndims: int, input_shape: Shape, input_dtype: DType
+    ) -> Tuple[LinearOperator, LinearOperator]:
         """Construct operators required by prox method."""
         w_input_shape = (
             # circular boundary: shape of input array
@@ -200,7 +202,7 @@ def prox(self, v: Array, lam: float = 1.0, **kwargs) -> Array:
         # Apply shrinkage to highpass component of shift-invariant Haar transform
         # of padded input (or to non-boundary region thereof for non-circular
         # boundary conditions).
-        WPv = self.WP(v)
+        WPv: Array = self.WP(v)
         WPv = WPv.at[slce].set(self.norm.prox(WPv[slce], snp.sqrt(2) * K * lam))
         u = (1.0 / K) * self.CWT(WPv)