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].

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 ~'},