我是深度学习、Keras 和图像处理的新手。我正在做一个项目，我尝试使用 CNN 补偿灰度图像中的运动伪影。因此，我有一个没有运动伪影的灰度图像作为标签。
但现在我不确定要使用哪个损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有意义吗？使用“均方对数误差”进行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 的准确率接近 0%。
是否有人在该领域有经验并且可以推荐一些文献或提出合适的损失函数和错误度量！？
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)
谢谢你的帮助！