diff --git a/docker-cpu.df b/docker-cpu.df
index e861ffa..dcda40a 100644
--- a/docker-cpu.df
+++ b/docker-cpu.df
@@ -26,8 +26,9 @@ RUN /opt/conda/bin/python3.5 -m pip install -q -r "requirements.txt"
 COPY enhance.py .
 
 # Get a pre-trained neural networks, non-commercial & attribution.
+RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne1x-photo-deblur-0.3.pkl.bz2"
 RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne1x-photo-repair-0.3.pkl.bz2"
 RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne2x-photo-default-0.3.pkl.bz2"
-
+RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne4x-photo-default-0.3.pkl.bz2"
 # Set an entrypoint to the main enhance.py script
 ENTRYPOINT ["/opt/conda/bin/python3.5", "enhance.py", "--device=cpu"]
diff --git a/docker-gpu.df b/docker-gpu.df
index 512388c..788a4d9 100644
--- a/docker-gpu.df
+++ b/docker-gpu.df
@@ -24,8 +24,10 @@ RUN /opt/conda/bin/python3.5 -m pip install -q -r "requirements.txt"
 COPY enhance.py .
 
 # Get a pre-trained neural networks, non-commercial & attribution.
+RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne1x-photo-deblur-0.3.pkl.bz2"
 RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne1x-photo-repair-0.3.pkl.bz2"
 RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne2x-photo-default-0.3.pkl.bz2"
+RUN wget -q "https://github.com/alexjc/neural-enhance/releases/download/v0.3/ne4x-photo-default-0.3.pkl.bz2"
 
 # Set an entrypoint to the main enhance.py script
 ENTRYPOINT ["/opt/conda/bin/python3.5", "enhance.py", "--device=gpu"]
diff --git a/enhance.py b/enhance.py
index 5d704d3..becb851 100755
--- a/enhance.py
+++ b/enhance.py
@@ -387,7 +387,7 @@ def load_model(self):
             error("Model file with pre-trained convolution layers not found. Download it here...",
                   "https://github.com/alexjc/neural-enhance/releases/download/v%s/%s"%(__version__, self.get_filename()))
         print('  - Loaded file `{}` with trained model.'.format(self.get_filename()))
-        return pickle.load(bz2.open(self.get_filename(), 'rb'))
+        return pickle.load(bz2.open(self.get_filename(absolute=True), 'rb'))
 
     def load_generator(self, params):
         if len(params) == 0: return
@@ -540,8 +540,8 @@ def match_histograms(self, A, B, rng=(0.0, 255.0), bins=64):
         (Ha, Xa), (Hb, Xb) = [np.histogram(i, bins=bins, range=rng, density=True) for i in [A, B]]
         X = np.linspace(rng[0], rng[1], bins, endpoint=True)
         Hpa, Hpb = [np.cumsum(i) * (rng[1] - rng[0]) ** 2 / float(bins) for i in [Ha, Hb]]
-        inv_Ha = scipy.interpolate.interp1d(X, Hpa, bounds_error=False)
-        map_Hb = scipy.interpolate.interp1d(Hpb, X, bounds_error=False)
+        inv_Ha = scipy.interpolate.interp1d(X, Hpa, bounds_error=False, fill_value='extrapolate')
+        map_Hb = scipy.interpolate.interp1d(Hpb, X, bounds_error=False, fill_value='extrapolate')
         return map_Hb(inv_Ha(A).clip(0.0, 255.0))
 
     def process(self, original):