scikit-image库----图像反卷积修复（二十一）

红太狼 2021-10-16 00:26 327阅读 0赞

#### 维纳过滤器（Wiener filter） ####

基于PSF（点扩散函数）的逆滤波器，先前的正则化（高频惩罚）以及数据与先前充分性之间的权衡。 必须手动调整正则化参数。  
在此示例中，我们使用Wiener和无监督的Wiener算法对图像的噪声版本进行去卷积。 该算法基于线性模型，不能像非线性方法（如电视恢复）那样恢复锐边，但速度要快得多。

import numpy as np
    import matplotlib.pyplot as plt
    %matplotlib inline
    from skimage import color, data, restoration
    
    astro = color.rgb2gray(data.astronaut())
    from scipy.signal import convolve2d as conv2
    psf = np.ones((5, 5)) / 25
    astro = conv2(astro, psf, 'same')
    astro += 0.1 * astro.std() * np.random.standard_normal(astro.shape)
    
    deconvolved, _ = restoration.unsupervised_wiener(astro, psf)
    
    fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 5),
                           sharex=True, sharey=True)
    
    plt.gray()
    
    ax[0].imshow(astro, vmin=deconvolved.min(), vmax=deconvolved.max())
    ax[0].axis('off')
    ax[0].set_title('Data')
    
    ax[1].imshow(deconvolved)
    ax[1].axis('off')
    ax[1].set_title('Self tuned restoration')
    
    fig.tight_layout()
    
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70]

#### Lucy-Richardson算法 ####

维纳滤波是频域的图像修复算法，而LR算法是时域的一种图像修复算法。该算法基于PSF（点扩散函数），PSF被描述为光学系统的脉冲响应。 通过多次迭代锐化模糊图像，需要手动调整

import numpy as np
    import matplotlib.pyplot as plt
    
    from scipy.signal import convolve2d as conv2
    
    from skimage import color, data, restoration
    
    astro = color.rgb2gray(data.astronaut())
    
    psf = np.ones((5, 5)) / 25
    astro = conv2(astro, psf, 'same')
    # Add Noise to Image
    astro_noisy = astro.copy()
    astro_noisy += (np.random.poisson(lam=25, size=astro.shape) - 10) / 255.
    
    # Restore Image using Richardson-Lucy algorithm
    deconvolved_RL = restoration.richardson_lucy(astro_noisy, psf, iterations=30)
    
    fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(18, 9))
    plt.gray()
    
    for a in (ax[0], ax[1], ax[2]):
           a.axis('off')
    
    ax[0].imshow(astro)
    ax[0].set_title('Original Data')
    
    ax[1].imshow(astro_noisy)
    ax[1].set_title('Noisy data')
    
    ax[2].imshow(deconvolved_RL, vmin=astro_noisy.min(), vmax=astro_noisy.max())
    ax[2].set_title('Restoration using\nRichardson-Lucy')
    
    
    fig.subplots_adjust(wspace=0.02, hspace=0.2,
                        top=0.9, bottom=0.05, left=0, right=1)
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70 1]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70]: /images/20211015/0e2395cd9e364af0a049305e071b8f8f.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70 1]: /images/20211015/b8fa48efa0f147d593b2822ed610655a.png