Estimate strength of blur

This example shows how the metric implemented in measure.blur_effect behaves, both as a function of the strength of blur and of the size of the re-blurring filter. This no-reference perceptual blur metric is described in 1.


Frederique Crete, Thierry Dolmiere, Patricia Ladret, and Marina Nicolas “The blur effect: perception and estimation with a new no-reference perceptual blur metric” Proc. SPIE 6492, Human Vision and Electronic Imaging XII, 64920I (2007) DOI:10.1117/12.702790

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.ndimage as ndi

import plotly
import as px
from skimage import (
    color, data, measure

Generate series of increasingly blurred images

Let us load an image available through scikit-image’s data registry. The blur metric applies to single-channel images.

Let us blur this image with a series of uniform filters of increasing size.

blurred_images = [ndi.uniform_filter(image, size=k) for k in range(2, 32, 2)]
img_stack = np.stack(blurred_images)

fig = px.imshow(
    labels={'animation_frame': 'blur strength ~'}