4.1 添加随机噪点

img = Image.open('1.jpg')  
img = img.resize((640, 640))  # 调小图像~先使原图变模糊
img_tensor = torch.tensor(np.array(img), dtype=torch.float).permute(2, 0, 1).unsqueeze(0)


noise = torch.randn_like(img_tensor)    # 与图像相同大小的随机标准正态分布噪点
noisy_img_tensor = img_tensor + noise   # 将噪点叠加到图像上
noisy_img = noisy_img_tensor.squeeze(0).permute(1, 2, 0).to(dtype=torch.uint8)
noisy_img = Image.fromarray(noisy_img.numpy())

4.2 图像去噪

# conv_kernel1 = torch.tensor([[1/16, 1/8, 1/16],
#                             [1/8, 1/4, 1/8],
#                             [1/16, 1/8, 1/16]], dtype=torch.float).unsqueeze(0).unsqueeze(0)
# # 生成随机3x3高斯分布
# random_gaussian = torch.randn(3, 3).unsqueeze(0).unsqueeze(0)
# print(random_gaussian)
font = FontProperties(fname='C:\Windows\Fonts\simkai.ttf', size=32)  # 使用楷体
plt.figure(figsize=(32, 32))  
plt.subplot(1, 3, 1)
plt.title('原始图像', fontproperties=font)
plt.imshow(img)
plt.axis('off')  
plt.subplot(1, 3, 2)
plt.title('噪点图像', fontproperties=font)
plt.imshow(noisy_img)
plt.axis('off')  
plt.subplot(1, 3, 3)
plt.title('去噪图像', fontproperties=font)
plt.imshow(conv2d(noisy_img_tensor, conv_kernel1))
plt.axis('off') 
plt.show()