scikit-image库--非局部去噪并保存纹理（五）

系统管理员 2021-10-18 11:38 347阅读 0赞

#### 非局部去噪并保存纹理 ####

在这个例子中，我们使用非局部均值滤波器对宇航员图像的细节进行去噪。非局部平均值算法将一个像素的值替换为一组其他像素值的平均值：以其他像素为中心的小补片与以感兴趣的像素为中心的补片进行比较，并且仅对具有接近当前像素的补片的像素执行平均值。T补丁。结果表明，该算法能很好地恢复纹理，而其它去噪算法会使纹理变得模糊。

import numpy as np
    import matplotlib.pyplot as plt
    
    from skimage import data, img_as_float
    from skimage.restoration import denoise_nl_means, estimate_sigma
    from skimage.measure import compare_psnr
    from skimage.util import random_noise
    
    
    astro = img_as_float(data.astronaut())
    astro = astro[30:180, 150:300]
    
    sigma = 0.08
    noisy = random_noise(astro, var=sigma**2)
    
    # estimate the noise standard deviation from the noisy image
    sigma_est = np.mean(estimate_sigma(noisy, multichannel=True))
    print("estimated noise standard deviation = {}".format(sigma_est))
    
    patch_kw = dict(patch_size=5,      # 5x5 patches
                    patch_distance=6,  # 13x13 search area
                    multichannel=True)
    
    # slow algorithm
    denoise = denoise_nl_means(noisy, h=1.15 * sigma_est, fast_mode=False,
                               **patch_kw)
    
    # slow algorithm, sigma provided
    denoise2 = denoise_nl_means(noisy, h=0.8 * sigma_est, sigma=sigma_est,
                                fast_mode=False, **patch_kw)
    
    # fast algorithm
    denoise_fast = denoise_nl_means(noisy, h=0.8 * sigma_est, fast_mode=True,
                                    **patch_kw)
    
    # fast algorithm, sigma provided
    denoise2_fast = denoise_nl_means(noisy, h=0.6 * sigma_est, sigma=sigma_est,
                                     fast_mode=True, **patch_kw)
    
    fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 6),
                           sharex=True, sharey=True)
    
    ax[0, 0].imshow(noisy)
    ax[0, 0].axis('off')
    ax[0, 0].set_title('noisy')
    ax[0, 1].imshow(denoise)
    ax[0, 1].axis('off')
    ax[0, 1].set_title('non-local means\n(slow)')
    ax[0, 2].imshow(denoise2)
    ax[0, 2].axis('off')
    ax[0, 2].set_title('non-local means\n(slow, using $\sigma_{est}$)')
    ax[1, 0].imshow(astro)
    ax[1, 0].axis('off')
    ax[1, 0].set_title('original\n(noise free)')
    ax[1, 1].imshow(denoise_fast)
    ax[1, 1].axis('off')
    ax[1, 1].set_title('non-local means\n(fast)')
    ax[1, 2].imshow(denoise2_fast)
    ax[1, 2].axis('off')
    ax[1, 2].set_title('non-local means\n(fast, using $\sigma_{est}$)')
    
    fig.tight_layout()
    
    # print PSNR metric for each case
    psnr_noisy = compare_psnr(astro, noisy)
    psnr = compare_psnr(astro, denoise)
    psnr2 = compare_psnr(astro, denoise2)
    psnr_fast = compare_psnr(astro, denoise_fast)
    psnr2_fast = compare_psnr(astro, denoise2_fast)
    
    print("PSNR (noisy) = {:0.2f}".format(psnr_noisy))
    print("PSNR (slow) = {:0.2f}".format(psnr))
    print("PSNR (slow, using sigma) = {:0.2f}".format(psnr2))
    print("PSNR (fast) = {:0.2f}".format(psnr_fast))
    print("PSNR (fast, using sigma) = {:0.2f}".format(psnr2_fast))
    
    plt.show()

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