# Towards Automatic Image Enhancement with Genetic Programming and Machine Learning
## 5 Discussion  
In this study, experiments have been carried out using four different metrics. Two of the metrics analyze the images from an aesthetic point of view (NIMA A and PHIL) and the other two from a quality point of view (NIMA T and BRISQUE). Table 2 and Figure 6 show how the two metrics with the highest standard deviation are BRISQUE and PHIL. The only metric specially trained for the real estate domain is PHIL. For its part, BRISQUE was trained from images of natural scenes.  
The first filters applied in this work for IE are the One-Click filter and a set of Classical filters. In both cases, the metrics that take into account the aesthetic value (NIMA A and PHIL) increase the mean value of the images (see Table 3 and Figure 7). However, with the averages based on image quality (NIMA T and BRISQUE), the mean value decreases after using these filters, especially in the case of BRISQUE.  
This trend is maintained when trying to improve the image with GAN trained on a One-Click basis (Table 3). GAN A increases the mean value of the images for the aesthetics metrics and decreases it for the quality metrics, but with lower levels of variation. Note that the GAN generates images much closer to the original image (SSIM of 0.90) and a BRISQUE value that, although negative, is closer to 0.  
Table 3 shows the results of combining GAN A with the Classical filters (GAN + CL), showing that, for the first time, the value of the quality metric BRISQUE is positive. The value of the other quality metric, NIMA T, is negative but close to 0. However, it is important to note that the SSIM is the lowest so far, indicating that the new images are quite far from the original. This very low SSIM (0.61) can be explained by the combination of the GAN with the Classical filters since the Classical filters independently already have a very low SSIM (0.66). This degree of difference with the original image, however, is not so clearly represented in the examples shown in Figure 9.  
The two aesthetics-based metrics (NIMA A and PHIL) provide higher values when extreme processing is applied to the images. This behavior may be due to the fact that the metrics were created based on real sets of images, which by nature do not usually include extreme processing. It is necessary to incorporate human-evaluated extreme examples in the training of the aesthetics metrics, in order to provide a reference of this type of images and, thus, avoid over-evaluation and consequent over-processing. Currently, examples with these extreme examples are incorporated in the PHIL metric updates.  
Concerning the experiments performed with GP, the choice of fitness completely conditions the behavior of the system. In the first case, GP.A, the fitness only tries to improve the result of an aesthetics metric: NIMA A. The result is an interesting filter from an artistic point of view, providing images with higher average scores in the two aesthetic metrics (NIMA A and PHIL) and in the quality metric BRISQUE. However, the resulting image presents a low similarity to the original, which manifests an unrealistic appearance (as can be seen in Figure 10). In fact, the lack of realism means that the image cannot be used in the context of real estate marketing, although it can be explored in a more artistic context.  
The other two fitness used seek to maintain a balance between image enhancement and similarity to the original. The fundamental difference is in the metric each uses to try to obtain a realistic result, with GP.B using the NIMA T metric, and GP.C using the SSIM. The latter mechanism is more robust, providing more realistic images (with an SSIM of 0.88), therefore being more appropriate for application in real estate problems.