diff --git a/suite2p/detection/denoise.py b/suite2p/detection/denoise.py
index ebd461df..3b510865 100644
--- a/suite2p/detection/denoise.py
+++ b/suite2p/detection/denoise.py
@@ -47,19 +47,15 @@ def pca_denoise(mov, block_size, n_comps_frac):
     maskMul = spatial_taper(Lyb // 4, Lyb, Lxb).numpy()
     norm = np.zeros((Ly, Lx), np.float32)
     reconstruction = np.zeros_like(mov)
-    block_re = np.zeros((nblocks, nframes, Lyb * Lxb))
     for i in range(nblocks):
         block = mov[:, yblock[i][0]:yblock[i][-1],
                     xblock[i][0]:xblock[i][-1]].reshape(-1, Lyb * Lxb)
         model = PCA(n_components=n_comps, random_state=0).fit(block)
-        block_re[i] = (block @ model.components_.T) @ model.components_
-        norm[yblock[i][0]:yblock[i][-1], xblock[i][0]:xblock[i][-1]] += maskMul
-
-    block_re = block_re.reshape(nblocks, nframes, Lyb, Lxb)
-    block_re *= maskMul
-    for i in range(nblocks):
+        block_re = ((block @ model.components_.T) @ model.components_).reshape(nframes, Lyb, Lxb)
+        block_re *= maskMul
         reconstruction[:, yblock[i][0]:yblock[i][-1],
-                       xblock[i][0]:xblock[i][-1]] += block_re[i]
+                       xblock[i][0]:xblock[i][-1]] += block_re
+        norm[yblock[i][0]:yblock[i][-1], xblock[i][0]:xblock[i][-1]] += maskMul
     reconstruction /= norm
     logger.info("Binned movie denoised (for cell detection only) in %0.2f sec." %
           (time.time() - t0))