Why is Gaussian Filter different between cv2 and skimage?

Question

I've got an image that I apply a Gaussian Blur to using both cv2.GaussianBlur and skimage.gaussian_filter libraries, but I get significantly different results. I'm curious as to why, and what can be done to make skimage look more like cv2. I know skimage.gaussian_filter is a wrapper around scipy.scipy.ndimage.filters.gaussian_filter. To clearly state the question, why are the two functions different and what can be done to make them more similar?

Here is my test image:

Here is the cv2 version (appears blurrier):

Here is the skimage/scipy version (appears sharper):

Details:

skimage_response = skimage.filters.gaussian_filter(im, 2, multichannel=True, mode='reflect')

cv2_response = cv2.GaussianBlur(im, (33, 33), 2)

So sigma=2 and the size of the filter is big enough that it shouldn't make a difference. Imagemagick covnert -gaussian-blur 0x2 visually agrees with cv2.

Versions: cv2=2.4.10, skimage=0.11.3, scipy=0.13.3

Answer 1

If anyone is curious about how to make skimage.gaussian_filter() match Matlab's equivalent imgaussfilt() (the reason I found this question), pass the parameter 'truncate=2' to skimage.gaussian_filter(). Both skimage and Matlab calculate the kernel size as a function of sigma. Matlab's default is 2. Skimage's default is 4, resulting in a significantly larger kernel by default.