我是深度学习和Keras和图像处理的新手。我正在研究一个项目，我尝试用CNN补偿灰度图像中的运动伪影。因此，我将灰度图像作为标签，没有运动伪影。
但是现在我不确定使用哪种丢失函数和什么样的错误度量。也许我需要某种2D互相关损失函数？或者像均方误差这样的损失函数是否有意义？ “平均对数误差”的第一次训练产生了视觉上良好的结果（预测看起来很像标签图像），但CNN的准确度为0％。
是否有人有该领域的经验，可以推荐一些文献或建议合适的损失函数和误差度量标准！？
如果我需要提供更详细的信息，请告诉我，我非常乐意这样做。
使用的CNN（有点像Unet）：
input_1 = Input((X_train.shape[1],X_train.shape[2], X_train.shape[3]))
conv1 = Conv2D(16, (3,3), strides=(2,2), activation='relu', padding='same')(input_1)
batch1 = BatchNormalization(axis=3)(conv1)
conv2 = Conv2D(32, (3,3), strides=(2,2), activation='relu', padding='same')(batch1)
batch2 = BatchNormalization(axis=3)(conv2)
conv3 = Conv2D(64, (3,3), strides=(2,2), activation='relu', padding='same')(batch2)
batch3 = BatchNormalization(axis=3)(conv3)
conv4 = Conv2D(128, (3,3), strides=(2,2), activation='relu', padding='same')(batch3)
batch4 = BatchNormalization(axis=3)(conv4)
conv5 = Conv2D(256, (3,3), strides=(2,2), activation='relu', padding='same')(batch4)
batch5 = BatchNormalization(axis=3)(conv5)
conv6 = Conv2D(512, (3,3), strides=(2,2), activation='relu', padding='same')(batch5)
drop1 = Dropout(0.25)(conv6)
upconv1 = Conv2DTranspose(256, (3,3), strides=(1,1), padding='same')(drop1)
upconv2 = Conv2DTranspose(128, (3,3), strides=(2,2), padding='same')(upconv1)
upconv3 = Conv2DTranspose(64, (3,3), strides=(2,2), padding='same')(upconv2)
upconv4 = Conv2DTranspose(32, (3,3), strides=(2,2), padding='same')(upconv3)
upconv5 = Conv2DTranspose(16, (3,3), strides=(2,2), padding='same')(upconv4)
upconv5_1 = concatenate([upconv5,conv2], axis=3)
upconv6 = Conv2DTranspose(8, (3,3), strides=(2,2), padding='same')(upconv5_1)
upconv6_1 = concatenate([upconv6,conv1], axis=3)
upconv7 = Conv2DTranspose(1, (3,3), strides=(2,2), activation='linear', padding='same')(upconv6_1)
model = Model(outputs=upconv7, inputs=input_1)
感谢您的帮助！