自编码器可以将彩色图像转为黑白图像。下面是一个 Python 实现的示例：

import numpy as np
import matplotlib.pyplot as plt
from keras.layers import Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Sequential


# 创建自编码器模型
def create_autoencoder(input_shape):
    model = Sequential()

    # Encoder
    model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=input_shape))
    model.add(MaxPooling2D((2, 2), padding='same'))
    model.add(Conv2D(16, (3, 3), activation='relu', padding='same'))
    model.add(MaxPooling2D((2, 2), padding='same'))
    model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
    model.add(MaxPooling2D((2, 2), padding='same'))

    # Decoder
    model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
    model.add(UpSampling2D((2, 2)))
    model.add(Conv2D(16, (3, 3), activation='relu', padding='same'))
    model.add(UpSampling2D((2, 2)))
    model.add(Conv2D(32, (3, 3), activation='relu'))
    model.add(UpSampling2D((2, 2)))
    model.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))

    model.compile(optimizer='adam', loss='binary_crossentropy')

    return model


# 加载数据
def load_data():
    # 加载彩色图像
    color_imgs = plt.imread('color_imgs.jpg')
    # 转换为黑白图像
    bw_imgs = np.dot(color_imgs[..., :3], [0.299, 0.587, 0.114])
    # 进行归一化
    bw_imgs = bw_imgs / 255.0
    # 转换为 tensor
    bw_imgs = np.reshape(bw_imgs, (-1, 224, 224, 1))

    return bw_imgs


if __name__ == '__main__':
    input_shape = (224, 224, 1)

    # 创建自编码器模型
    model = create_autoencoder(input_shape)

    # 加载数据
    bw_imgs = load_data()

    # 训练模型
    model