diff --git a/modopt/opt/linear/wavelet.py b/modopt/opt/linear/wavelet.py
index cb75ecff..3fa2d60d 100644
--- a/modopt/opt/linear/wavelet.py
+++ b/modopt/opt/linear/wavelet.py
@@ -104,6 +104,9 @@ def __init__(self,
         self.op = self.operator.op
         self.adj_op = self.operator.adj_op
 
+    @property
+    def coeffs_shape(self):
+        return self.operator.coeffs_shape
 
 class CPUWaveletTransform(LinearParent):
     """
@@ -283,6 +286,7 @@ def __init__(
         self.level = level
         self.shape = shape
         self.mode = mode
+        self.coeffs_shape = None # will be set after op.
 
     def op(self, data: torch.Tensor) -> list[torch.Tensor]:
         """Apply the wavelet decomposition on.
@@ -308,8 +312,6 @@ def op(self, data: torch.Tensor) -> list[torch.Tensor]:
                 coeffs_ = ptwt.wavedec2(
                     data_, self.wavelet, level=self.level, mode=self.mode, axes=(-3, -2)
                 )
-                self.coeffs_shape = [coeffs_[0].shape]
-                self.coeffs_shape += [tuple(cc.shape for cc in c) for c in coeffs_]
                 # flatten list of tuple of tensors to a list of tensors
                 coeffs = [torch.view_as_complex(coeffs_[0].contiguous())] + [
                     torch.view_as_complex(cc.contiguous())
@@ -418,6 +420,7 @@ def __init__(
         self.mode = mode
 
         self.operator = TorchWaveletTransform(shape=shape, wavelet=wavelet, level=level,mode=mode)
+        self.coeffs_shape = None # will be set after op
 
     def op(self, data: cp.array) -> cp.ndarray:
         """Define the wavelet operator.