simple_convolution.py

#!/usr/bin/env python
import numpy as np
import cv2
from scipy.signal import convolve2d
from skimage import color, data, restoration
import console

# read input
frame = cv2.imread("input.jpg").astype(np.float32) / 255.0

# construct a 5x5 box blur kernel
psf = np.ones((5,5)) / 25

# convolve each channel with the kernel
for i in range(frame.shape[-1]):
    frame[:,:,i] = convolve2d(frame[:,:,i], psf, mode="same")

# add gaussian noise
frame += 0.1 * frame.std() * np.random.standard_normal(frame.shape)

# wiener deconv
fixed = np.zeros(frame.shape)
for i in range(frame.shape[-1]):
    fixed[:,:,i], _ = restoration.unsupervised_wiener(frame[:,:,i], psf)

# save output
fixed = np.clip(fixed * 255.0,0,255).astype(np.uint8)
cv2.imwrite("output.jpg", fixed)

frame = frame.astype(np.float32)
for i in range(frame.shape[-1]):
    frame[:,:,i] = convolve2d(frame[:,:,i], psf, mode='same')
frame += 0.1 * frame.std() * np.random.standard_normal(frame.shape)
fixed = skimage.restoration.unsupervised_wiener(frame, psf)

This is what I did, and it still throws the ValueError.

Try replacing:

fixed = skimage.restoration.unsupervised_wiener(frame, psf)

With:

fixed = np.zeros(frame.shape)
for i in range(frame.shape[-1]):
    fixed[:,:,i] = skimage.restoration.unsupervised_wiener(frame[:,:,i], psf)

If I do that, I get this back:
fixed[:,:,i] = skimage.restoration.unsupervised_wiener(frame[:,:,i], psf)
ValueError: could not broadcast input array from shape (2) into shape (1080,1920)

Ah, oops, I forgot that it returns two values. You can fix that as follows (referencing this example http://scikit-image.org/docs/0.10.x/auto_examples/plot_restoration.html):

fixed = np.zeros(frame.shape)
for i in range(frame.shape[-1]):
    fixed[:,:,i], _ = skimage.restoration.unsupervised_wiener(frame[:,:,i], psf)

I've updated the original post to be a working example including deconvolution.

Thank you so much. Can you please add your snippet as an answer on StackOverflow so I can mark it as a solution?