diff --git a/examples/bmode_reconstruction_example.py b/examples/bmode_reconstruction_example.py
index 16073ffb..8a9707ca 100644
--- a/examples/bmode_reconstruction_example.py
+++ b/examples/bmode_reconstruction_example.py
@@ -16,7 +16,7 @@
 from kwave.utils.dotdictionary import dotdict
 from kwave.utils.signals import tone_burst, get_win
 from kwave.utils.filters import gaussian_filter
-from kwave.reconstruction.tools import log_compression
+from kwave.reconstruction.tools import log_compression, db
 from kwave.reconstruction.beamform import envelope_detection
 
 
@@ -197,8 +197,8 @@ def main():
     # Frequency Filtering
     # -----------------------------
 
-    scan_lines_fund = gaussian_filter(scan_lines.T, 1/kgrid.dt, tone_burst_freq, 100)
-    scan_lines_harm = gaussian_filter(scan_lines.T, 1/kgrid.dt, 2 * tone_burst_freq, 30)  # plotting was not impl.
+    scan_lines_fund = gaussian_filter(scan_lines, 1/kgrid.dt, tone_burst_freq, 100)
+    scan_lines_harm = gaussian_filter(scan_lines, 1/kgrid.dt, 2 * tone_burst_freq, 30)  # plotting was not impl.
 
     # -----------------------------
     # Envelope Detection
@@ -221,6 +221,8 @@ def main():
 
     # Set the desired size of the image
     image_size = kgrid.size 
+    harm_img = db(scan_lines_harm.T)
+    fund_img = db(scan_lines_fund.T)
 
     # Create the axis variables
     x_axis = [0, image_size[0] * 1e3] # [mm]      
@@ -235,12 +237,13 @@ def main():
     plt.xlabel('Image width [mm]')
     plt.ylabel('Depth [mm]')
     plt.subplot(1, 3, 2)
-    plt.imshow(scan_lines_fund, cmap='bone', aspect='auto',  extent=[y_axis[0], y_axis[1], x_axis[1], x_axis[0]])
+    plt.imshow(fund_img, cmap='bone', aspect='auto',  extent=[y_axis[0], y_axis[1], x_axis[1], x_axis[0]],
+               vmax=np.max(fund_img), vmin=np.max(fund_img)-40)
     plt.xlabel('Image width [mm]')
     plt.title('Fundamental')
     plt.yticks([])
     plt.subplot(1, 3, 3)
-    plt.imshow(scan_lines_harm, cmap='bone', aspect='auto',
+    plt.imshow(harm_img, cmap='bone', aspect='auto', vmax=np.max(harm_img), vmin=np.max(harm_img)-40,
                 extent=[y_axis[0], y_axis[1], x_axis[1], x_axis[0]])
     plt.yticks([])
     plt.xlabel('Image width [mm]')
diff --git a/kwave/reconstruction/tools.py b/kwave/reconstruction/tools.py
index 3101190d..b05b92ca 100644
--- a/kwave/reconstruction/tools.py
+++ b/kwave/reconstruction/tools.py
@@ -14,12 +14,16 @@ def log_compression(signal, cf, normalize=False):
    Returns: signal: log-compressed signal
     """
     if normalize:
-        ms = np.max(signal, axis=0)
+        ms = np.max(signal, axis=-1)
+        if np.ndim(signal) == 2:
+            ms = ms[:, np.newaxis]
         signal = ms * (np.log10(1 + cf * signal / ms) / np.log10(1 + cf))
     else:
         signal = np.log10(1 + cf * signal) / np.log10(1 + cf)
     return signal
 
+def db(x):
+    return 20 * np.log10(np.abs(x))
 
 def apodize(distance, aperture, window):
     """
diff --git a/kwave/utils/filters.py b/kwave/utils/filters.py
index 660a3dc4..2c814e1f 100644
--- a/kwave/utils/filters.py
+++ b/kwave/utils/filters.py
@@ -374,7 +374,7 @@ def gaussian_filter(signal: Union[np.ndarray, List[float]],
     applied to each matrix row.
 
     Args:
-        signal:         Signal to filter
+        signal:         Signal to filter [channel, samples]
         Fs:             Sampling frequency [Hz]
         frequency:      Center frequency of filter [Hz]
         bandwidth:      Bandwidth of filter in percentage
@@ -384,7 +384,7 @@ def gaussian_filter(signal: Union[np.ndarray, List[float]],
 
     """
 
-    N = len(signal)
+    N = signal.shape[-1]
     if N % 2 == 0:
         f = np.arange(-N / 2, N / 2) * Fs / N
     else:
@@ -399,10 +399,10 @@ def gaussian_filter(signal: Union[np.ndarray, List[float]],
 
     # add dimensions to filter to be broadcastable to signal shape
     if len(signal.shape) == 2:
-        gfilter = gfilter[:, np.newaxis]
+        gfilter = gfilter[np.newaxis,:]
 
     # apply filter
-    signal = np.real(ifft(ifftshift(gfilter.T * fftshift(fft(signal.T))))).T
+    signal = np.real(ifft(ifftshift(gfilter * fftshift(fft(signal)))))
 
     return signal