Matplotlib Image Antialiasing Tutorial

Introduction

This tutorial will guide you through the process of antialiasing an image using Matplotlib in Python. Antialiasing is a technique used to smooth out jagged edges and reduce distortion in images. In this tutorial, we will use Matplotlib to generate a 450x450 pixel image with varying frequency content. We will then subsample the image from 450 data pixels to either 125 pixels or 250 pixels to demonstrate how antialiasing can be used to reduce the Moiré patterns caused by high-frequency data being subsampled.

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Generate Image

First, we need to generate a 450x450 pixel image with varying frequency content using NumPy.

import matplotlib.pyplot as plt
import numpy as np

N = 450
x = np.arange(N) / N - 0.5
y = np.arange(N) / N - 0.5
aa = np.ones((N, N))
aa[::2, :] = -1

X, Y = np.meshgrid(x, y)
R = np.sqrt(X**2 + Y**2)
f0 = 5
k = 100
a = np.sin(np.pi * 2 * (f0 * R + k * R**2 / 2))
a[:int(N / 2), :][R[:int(N / 2), :] < 0.4] = -1
a[:int(N / 2), :][R[:int(N / 2), :] < 0.3] = 1
aa[:, int(N / 3):] = a[:, int(N / 3):]
a = aa

Subsample Image with 'nearest' Interpolation

Now, we will subsample the image from 450 data pixels to 125 pixels or 250 pixels using 'nearest' interpolation. This will demonstrate how the high-frequency data being subsampled can cause Moiré patterns.

fig, axs = plt.subplots(2, 2, figsize=(5, 6), layout='constrained')
axs[0, 0].imshow(a, interpolation='nearest', cmap='RdBu_r')
axs[0, 0].set_xlim(100, 200)
axs[0, 0].set_ylim(275, 175)
axs[0, 0].set_title('Zoom')

for ax, interp, space in zip(axs.flat[1:],
                             ['nearest', 'antialiased', 'antialiased'],
                             ['data', 'data', 'rgba']):
    ax.imshow(a, interpolation=interp, interpolation_stage=space,
              cmap='RdBu_r')
    ax.set_title(f"interpolation='{interp}'\nspace='{space}'")
plt.show()

Subsample Image with 'antialiased' Interpolation

Next, we will subsample the image from 450 data pixels to 125 pixels or 250 pixels using 'antialiased' interpolation. This will demonstrate how antialiasing can be used to reduce the Moiré patterns caused by high-frequency data being subsampled.

fig, axs = plt.subplots(2, 2, figsize=(5, 6), layout='constrained')
axs[0, 0].imshow(a, interpolation='nearest', cmap='RdBu_r')
axs[0, 0].set_xlim(100, 200)
axs[0, 0].set_ylim(275, 175)
axs[0, 0].set_title('Zoom')

for ax, interp, space in zip(axs.flat[1:],
                             ['nearest', 'antialiased', 'antialiased'],
                             ['data', 'data', 'rgba']):
    ax.imshow(a, interpolation=interp, interpolation_stage=space,
              cmap='RdBu_r')
    ax.set_title(f"interpolation='{interp}'\nspace='{space}'")
plt.show()

Upsample Image with 'nearest' Interpolation

Now, we will upsample the image from 500 data pixels to 530 rendered pixels using 'nearest' interpolation. This will demonstrate how the Moiré patterns can still occur even when the image is upsampled if the upsampling factor is not an integer.

fig, ax = plt.subplots(figsize=(6.8, 6.8))
ax.imshow(a, interpolation='nearest', cmap='gray')
ax.set_title("upsampled by factor a 1.048, interpolation='nearest'")
plt.show()

Upsample Image with 'antialiased' Interpolation

Finally, we will upsample the image from 500 data pixels to 530 rendered pixels using 'antialiased' interpolation. This will demonstrate how using better antialiasing algorithms can reduce the Moiré patterns.

fig, ax = plt.subplots(figsize=(6.8, 6.8))
ax.imshow(a, interpolation='antialiased', cmap='gray')
ax.set_title("upsampled by factor a 1.048, interpolation='antialiased'")
plt.show()

Summary

In this tutorial, we learned how to use Matplotlib to antialias an image to reduce Moiré patterns caused by subsampling high-frequency data. We generated a 450x450 pixel image with varying frequency content, and subsampled the image from 450 data pixels to either 125 pixels or 250 pixels using 'nearest' and 'antialiased' interpolation. We also demonstrated how upsampling an image using 'nearest' interpolation can still lead to Moiré patterns, but using better antialiasing algorithms can reduce these effects.

Matplotlib Image Antialiasing