## 4 Experimental Results and Discussion
### Discussion  
Extensive discussion of our proposed two-phase deep color correction network for tongue images is made in this section to provide further insights into the potential of further work.  
* Two-phase framework vs. single-phase framework The two-phase framework proposed in this paper enables the color correction methods to deal with both quantitative chromatic correction and perceptual color adaptability. The traditional single-phase framework focuses on establishing a mapping from the input image to the target color space to obtain the preferred results of color constancy or perception. The color constancy and flexibility of adaptive perceptual colors are difficult to be compatible with each other.
* Deep network on the OCC A simple and efficient deep convolutional neural network is designed for the OCC phase. The architecture design of deep network for specific applications remains an open problem. We don't think that the color correction problem is a very complicated problem, and it has been solved through a lookup table. Therefore, our principle is to find low-capacity models. In other words, the architecture of a deep neural network should be as simple as possible. As can be seen from the experimental results in Table 2, there is a trade-off between the number of hidden layers and performance. Other hyper parameters of the network, such as the size and number of filters in each layer, can also be determined through experimental results. However, a priori insight into the color correction of tongue images and the interpretability of deep networks may help design a more reasonable architecture.  
Figure 16: The PCC results with typical recommended user parameters.  
Figure 15: The PCC results with typical recommended user parameters.  
* Objective accuracy and robustness From the objective performance in Section 4.2, we can be confident of the power of deep networks in OCC. From Tables 2-7, the average color difference and maximum color difference will be greatly reduced in different situations. Our OCC achieves high accuracy and robustness. In our experiments, we trained a unique model for each experimental scenario. However, due to the simple structure, the training quickly converged. To simplify the training process, users can try training a single model for all scenarios. The accuracy may decrease slightly. In future work, you can develop automated training applications for end users.
* Evaluation metrics The purpose of the first phase is to reduce the objective color distortion for further automatic tongue image analysis. In this phase, reducing the chromatic distance is critical. Although chromatic distance is widely used for color correction of the tongue image [16; 26], it also makes it easy to use in order to compare our method with other methods. According to related research on calculating color constancy, it may be a promising approach to design a comprehensive experiment to solve the tongue image color correction.
* Perceptual color correction In the evaluation of the perceptual color correction, subjective results for several tongue images are employed. Furthermore, we also showed a set of corrected tongue samples to the TCM doctors at the Xuanwu Hospital Capital Medical University. They confirmed that the corrected images can be used for diagnosis. More professional and in-depth subjective study on the effectiveness of the tongue image acquisition and analysis system will be considered in our future work.
* Other potential applications Although TDCCN is proposed for the tongue image correction, it has the potential to be widely used for computing color constancy and color-related computer vision applications. For example, in the design of anti-counterfeit color packaging, chromaticity accuracy and perceived color difference should be considered at the same time. The proposed OCC and PCC can be used separately. The OCC model provides high accuracy and robustness for quantitative color analysis, while the PCC provides a flexible tool for perceptual color design.