Deblur an image using scikit-image

I am trying to use skimage.restoration.wiener, but I always end up with an image with a bunch of 1 (or -1), what am I doing wrong? The original image comes from Uni of Waterloo.

import numpy as np
from scipy.misc import imread
from skimage import color, data, restoration
from scipy.signal import convolve2d as conv2

def main():
  image = imread("/Users/gsamaras/Downloads/boat.tif")
  psf = np.ones((5, 5)) / 25
  image = conv2(image, psf, 'same')
  image += 0.1 * image.std() * np.random.standard_normal(image.shape)

  deconvolved = restoration.wiener(image, psf, 0.00001)
  print deconvolved
  print image

if __name__ == "__main__":
    main()

Output:

[[ 1. -1.  1. ...,  1. -1. -1.]
 [-1. -1.  1. ..., -1.  1.  1.]
 [ 1.  1.  1. ...,  1.  1.  1.]
 ..., 
 [ 1.  1.  1. ...,  1. -1.  1.]
 [ 1.  1.  1. ..., -1.  1. -1.]
 [ 1.  1.  1. ..., -1.  1.  1.]]
[[  62.73526298   77.84202199   94.1563234  ...,   85.12442365
    69.80579057   48.74330501]
 [  74.79638704  101.6248559   143.09978769 ...,  100.07197414
    94.34431216   59.72199141]
 [  96.41589893  132.53865314  161.8286996  ...,  137.17602535
   117.72691238   80.38638741]
 ..., 
 [  82.87641732  122.23168689  146.14129645 ...,  102.01214025
    75.03217549   59.78417916]
 [  74.25240964  100.64285679  127.38475015 ...,   88.04694654
    66.34568789   46.72457454]
 [  42.53382524   79.48377311   88.65000364 ...,   50.84624022
    36.45044106   33.22771889]]

And I tried several values. What am I missing?

My best so far solution is:

import numpy as np
#import matplotlib.pyplot as plt
from scipy.misc import imfilter, imread
from skimage import color, data, restoration
from scipy.signal import convolve2d as conv2

def main():
  image = imread("/Users/gsamaras/Downloads/boat.tif")
  #plt.imshow(arr, cmap='gray')
  #plt.show()
  #blurred_arr = imfilter(arr, "blur")
  psf = np.ones((5, 5)) / 25
  image = conv2(image, psf, 'same')
  image += 0.1 * image.std() * np.random.standard_normal(image.shape)

  deconvolved = restoration.wiener(image, psf, 1, clip=False)
  #print deconvolved
  plt.imshow(deconvolved, cmap='gray')
  plt.show()
  #print image

if __name__ == "__main__":
    main()

Much smaller values in restoration.wiener() lead to images that appear like you have put a non-transparent overlay above it (like this). On the other hand as this value grows the image blurs more and more. A value near 1 seems to work best and deblur the image.

Worthnoting is the fact that the smaller this value (I mean the balance, the greater the image size is.

_{PS - I am open to new answers.}