模糊图像在超像素池化网络(spn)中提取特征后，如何进行去模糊操作。我需要上述描述的pytorch代码

对于模糊图像的去模糊操作，可以使用一些经典的算法，比如基于深度学习的去模糊方法或者基于传统图像处理的去模糊方法。但是，在超像素池化网络中提取特征后，我们可以考虑使用一些基于超像素的去模糊方法，比如基于超像素的非局部均值去模糊方法。

下面是一个基于超像素的非局部均值去模糊方法的 PyTorch 代码示例：

import torch
import numpy as np
from skimage.segmentation import slic
from skimage.color import rgb2lab, lab2rgb

def deblur(image, spn_features, sp_size=20, reg=0.1):
    # Convert the image to LAB color space
    lab_image = rgb2lab(image)

    # Compute the superpixels using SLIC algorithm
    segments = slic(image, n_segments=sp_size, compactness=10)

    # Compute the mean color of each superpixel
    sp_colors = np.zeros((sp_size, 3))
    for i in range(sp_size):
        sp_mask = (segments == i)
        sp_colors[i,:] = np.mean(lab_image[sp_mask], axis=0)

    # Compute the non-local means weights
    weights = np.zeros((sp_size, sp_size))
    for i in range(sp_size):
        for j in range(sp_size):
            if i == j:
                weights[i,j] = 1
            else:
                diff = sp_colors[i,:] - sp_colors[j,:]
                dist = np.sum(diff**2)
                weights[i,j] = np.exp(-dist / (2*reg**2))

    # Normalize the weights
    weights = weights / np.sum(weights, axis=1, keepdims=True)

    # Apply the non-local means filter to each superpixel
    filtered_sp_colors = np.zeros((sp_size, 3))
    for i in range(sp_size):
        filtered_sp_colors[i,:] = np.sum(weights[i,:] * sp_colors, axis=0)

    # Replace the colors of the superpixels in the image
    filtered_image = np.zeros_like(lab_image)
    for i in range(sp_size):
        sp_mask = (segments == i)
        filtered_image[sp_mask,:] = filtered_sp_colors[i,:]

    # Convert the image back to RGB color space
    filtered_image = lab2rgb(filtered_image)

    return filtered_image

此代码中，我们使用了 SLIC 算法来计算超像素，并计算了每个超像素的平均颜色。然后，我们计算了非局部均值权重，并将其用于每个超像素的颜色。最后，我们将过滤后的颜色替换超像素的颜色，并将图像转换回 RGB 颜色空间。