f292829e959321ff1376d263fc40ecc21cf794fa,doc/examples/filters/plot_denoise.py,,,#,36

Before Change

ax[0, 2].imshow(denoise_bilateral(noisy, sigma_color=0.05, sigma_spatial=15))
ax[0, 2].axis("off")
ax[0, 2].set_title("Bilateral")
ax[0, 3].imshow(denoise_wavelet(noisy, sigma=0.4*astro.std(),
                                multichannel=True))
ax[0, 3].axis("off")
ax[0, 3].set_title("Wavelet")

After Change

astro = img_as_float(data.astronaut())
astro = astro[220:300, 220:320]

sigma = 0.155
noisy = random_noise(astro, var=sigma**2)

fig, ax = plt.subplots(nrows=2, ncols=4, figsize=(8, 5), sharex=True,
                       sharey=True, subplot_kw={"adjustable": "box-forced"})

plt.gray()

// Estimate the average noise standard deviation across color channels.
sigma_est = estimate_sigma(noisy, multichannel=True, average_sigmas=True)
// Due to clipping in random_noise, the estimate will be a bit smaller than the
// specified sigma.
print("Estimated Gaussian noise standard deviation = {}".format(sigma_est))

ax[0, 0].imshow(noisy)
ax[0, 0].axis("off")
ax[0, 0].set_title("noisy")
ax[0, 1].imshow(denoise_tv_chambolle(noisy, weight=0.1, multichannel=True))
ax[0, 1].axis("off")
ax[0, 1].set_title("TV")
ax[0, 2].imshow(denoise_bilateral(noisy, sigma_color=0.05, sigma_spatial=15))
ax[0, 2].axis("off")
ax[0, 2].set_title("Bilateral")
ax[0, 3].imshow(denoise_wavelet(noisy, sigma=0.85*sigma_est,
                                multichannel=True))
ax[0, 3].axis("off")
ax[0, 3].set_title("Wavelet")

ax[1, 1].imshow(denoise_tv_chambolle(noisy, weight=0.2, multichannel=True))
ax[1, 1].axis("off")
ax[1, 1].set_title("(more) TV")
ax[1, 2].imshow(denoise_bilateral(noisy, sigma_color=0.1, sigma_spatial=15))
ax[1, 2].axis("off")
ax[1, 2].set_title("(more) Bilateral")
ax[1, 3].imshow(denoise_wavelet(noisy, sigma=1.25*sigma_est,
                                multichannel=True))
ax[1, 3].axis("off")
ax[1, 3].set_title("(more) Wavelet")

Instances