Add image enhancement measure (EME)

doc/examples/filters/plot_enhancement_measure.py

@@ -0,0 +1,61 @@
+"""
+=========================================
+Quantitative Measure of Image Enhancement
+=========================================
+In this example, we will quantify the effect of applying contrast enhancement
+to an image using a measure called EME [1]_.
+
+It is defined as the log of the ratio of local maximum to minimum within a
+sliding window of given size. This measure aims to provide a perceptual
+estimate of image quality, useful when comparing image enhancement algorithms.
+
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage import data
+from skimage.filters import gaussian
+from skimage.exposure import equalize_adapthist
+from skimage.metrics import simple_metrics
+
+img_gray = data.camera()
+img_rgb = data.astronaut()
+
+
+def compare_side_by_side(before: np.ndarray, after: np.ndarray):
+    fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 14))
+    for idx, image in enumerate([before, after]):
+        ax[idx].imshow(image)
+        ax[idx].axis('off')
+        ax[idx].set_title("EME = {:.2f} ".format(
+            simple_metrics.enhancement_measure(image, size=3)))
+    fig.show()
+
+
+#####################################################################
+# We will create a function that takes an original image as `before`
+# and applies a `transform` to it. We will use a gaussian filter to
+# strongly blur the image and an adaptive histogram equalization as
+# transforms.
+
+
+compare_side_by_side(before=img_gray, after=gaussian(image=img_gray,
+                                                     sigma=10))
+compare_side_by_side(before=img_rgb, after=equalize_adapthist(image=img_rgb,
+                                                              nbins=256 * 3))
+
+############################################################################
+# You can see that the greyscale camera image is very blurred due to
+# strong gaussian filtering. This resulted in smoothing a lot of pixel
+# differences and, as a result, to lower value of EME.
+# On the other hand, the `astronaut` image was enhanced by adaptive
+# histogram equalization with `256*3` bins. Histogram equalization
+# brought out details and the EME value is higher than before the transform.
+#
+# References
+# ----------
+#
+# .. [1] Sos S. Agaian, Karen Panetta, and Artyom M. Grigoryan.
+#        "A new measure of image enhancement.",
+#        IASTED International Conference on Signal Processing
+#        & Communication, Citeseer, 2000,
+#        :DOI:`10.1.1.35.4021`

skimage/metrics/__init__.py

@@ -4,7 +4,8 @@
 from ._variation_of_information import variation_of_information
 from .set_metrics import hausdorff_distance, hausdorff_pair
 from .simple_metrics import (mean_squared_error, normalized_mutual_information,
-                             normalized_root_mse, peak_signal_noise_ratio)
+                             normalized_root_mse, peak_signal_noise_ratio,
+                             enhancement_measure)
 
 __all__ = [
     "adapted_rand_error",
@@ -14,6 +15,7 @@
     "normalized_mutual_information",
     "normalized_root_mse",
     "peak_signal_noise_ratio",
+    "enhancement_measure",
     "structural_similarity",
     "hausdorff_distance",
     "hausdorff_pair",

skimage/metrics/simple_metrics.py

@@ -1,13 +1,17 @@
 import numpy as np
 from scipy.stats import entropy
+from scipy.ndimage import maximum_filter, minimum_filter
 
+from ..exposure import rescale_intensity
+from ..util import img_as_float
 from ..util.dtype import dtype_range
 from .._shared.utils import _supported_float_type, check_shape_equality, warn
 
 __all__ = ['mean_squared_error',
            'normalized_root_mse',
            'peak_signal_noise_ratio',
            'normalized_mutual_information',
+           'enhancement_measure',
            ]
 
 
@@ -259,3 +263,53 @@ def normalized_mutual_information(image0, image1, *, bins=100):
     H01 = entropy(np.reshape(hist, -1))
 
     return (H0 + H1) / H01
+
+
+def enhancement_measure(image: np.ndarray,
+                        size: int = 3,
+                        eps: float = 1e-6) -> float:
+    """ The image enhancement measure called EME based on [1]_.
+    It is a way of quantifying improvement of the image after enhancement.
+
+    The function uses a sliding window of user-provided size to measure
+    the mean of log of maximal and minimal intensity ratio
+    within the window.
+
+    Parameters
+    ----------
+    image : array
+        Input image of which the quality should be assessed.
+        Can be either 3-channel RGB or 1-channel grayscale.
+    size : int, optional
+        Size of the window.
+    eps : float, optional
+        Parameter to avoid division by zero.
+
+    Returns
+    -------
+    eme : float
+        The number describing image quality.
+
+    References
+    ----------
+    .. [1] Sos S. Agaian,  Karen Panetta, and Artyom M. Grigoryan.
+            "A new measure of image enhancement.",
+            IASTED International Conference on Signal Processing
+            & Communication, Citeseer, 2000,
+
+    Examples
+    --------
+    >>> from skimage.data import camera
+    >>> from skimage.exposure import equalize_hist
+    >>> img = camera()
+    >>> before = enhancement_measure(img)
+    >>> after = enhancement_measure(equalize_hist(img))
+    >>> before < after
+    True
+    """
+    image = img_as_float(image)
+    image = rescale_intensity(image, out_range=(0., 1.))
+    eme = np.divide(maximum_filter(image, size=size),
+                    minimum_filter(image, size=size) + eps)
+    eme = np.mean(20 * np.log(eme + eps))
+    return eme

skimage/metrics/tests/test_simple_metrics.py

@@ -4,8 +4,11 @@
 
 from skimage import data
 from skimage._shared._warnings import expected_warnings
+from skimage._shared.testing import assert_less
 from skimage.metrics import (peak_signal_noise_ratio, normalized_root_mse,
-                             mean_squared_error, normalized_mutual_information)
+                             mean_squared_error, normalized_mutual_information,
+                             enhancement_measure)
+from skimage.exposure import equalize_hist, adjust_gamma
 
 
 np.random.seed(5)
@@ -137,3 +140,23 @@ def test_nmi_random_3d():
         1,
         decimal=2,
     )
+
+
+def test_EME_greyscale():
+    orig = cam
+    enhanced = equalize_hist(orig)
+    assert_less(enhancement_measure(orig),
+                enhancement_measure(enhanced))
+    assert_less(enhancement_measure(orig, size=5),
+                enhancement_measure(enhanced, size=5))
+    assert_less(enhancement_measure(orig, size=11),
+                enhancement_measure(enhanced, size=11))
+
+
+def test_EME_color():
+    orig = data.astronaut()
+    enhanced = adjust_gamma(orig, gamma=5)
+    assert_less(enhancement_measure(orig, size=3),
+                enhancement_measure(enhanced, size=3, eps=1))
+    assert_less(enhancement_measure(orig, size=7),
+                enhancement_measure(enhanced, size=7))