#### 谢飞

q在B蓝色图像通道上的卷积计算过程如公式1所示：

```import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib
class Image_combine():
def __init__(self,img_original_path,save_path):
self.img_original_path=img_original_path
self.save_path=save_path
img_original=np.array(Image.open(img_original_path))
self.img_original_R=img_original.transpose([2,0,1])[0,:,:]#pick out the Red channel
self.img_original_G=img_original.transpose([2,0,1])[1,:,:]#pick out the Green channel
self.img_original_B=img_original.transpose([2,0,1])[2,:,:]#pick out the Blue channel
self.min_original=self.img_original_R+self.img_original_B-self.img_original_G#R+B-G
elif channel=="RGB_original":
elif channel=="RGB_original_confuse":
img_new_1=img_new_1.transpose([1,2,0])
img_new_1=img_new_1.astype("int32").numpy()
img_new_1[img_new_1>255]=255
img_new_1[img_new_1<0]=0
#plt.imshow(img_new_1)
plt.imsave(self.save_path+self.img_original_path[14:-4]+"_deal_"+channel+".jpg",img_new_1/255)
#matplotlib.image.imsave(self.save_path+self.img_original_path[15:-4]+"_deal_"+channel+".jpg",img_new_1.astype("float32"))
elif channel=="RGB_original":
elif channel=="RGB_original_confuse":
img_new_1=img_new_1.transpose([1,2,0])
img_new_1=img_new_1.astype("int32").numpy()
img_new_1[img_new_1>255]=255
img_new_1[img_new_1<0]=0
plt.imshow(img_new_1)
plt.imsave(self.save_path+self.img_original_path[14:-4]+"_deal_"+channel+".jpg",img_new_1/255)
return img_new_1```

#### 相关链接

https://www.sciencedirect.com/science/article/pii/S0301679X22002298