fixes weird issue of CAS and bicubic interpolation

essentials.py

@@ -296,13 +296,13 @@ def min_(tensor_list):
     # return the element-wise min of the tensor list.
     x = torch.stack(tensor_list)
     mn = x.min(axis=0)[0]
-    return mn
+    return torch.clamp(mn, min=0)
     
 def max_(tensor_list):
     # return the element-wise max of the tensor list.
     x = torch.stack(tensor_list)
     mx = x.max(axis=0)[0]
-    return mx
+    return torch.clamp(mx, max=1)
 
 # From https://github.com/Jamy-L/Pytorch-Contrast-Adaptive-Sharpening/
 class ImageCAS:
@@ -354,7 +354,7 @@ class ImageCAS:
 
         output = ((b + d + f + h)*w + e) * div
         output = output.clamp(0, 1)
-        #output = torch.nan_to_num(output)   # what am I doing?!
+        #output = torch.nan_to_num(output)   # this seems the only way to ensure there are no NaNs
 
         output = pb(output)