# Towards Automatic Image Enhancement with Genetic Programming and Machine Learning
## 6 Conclusions  
This work proposes an automatic image enhancement approach that takes an input image and generates an output suitable for a real-world application, specifically for real estate image marketing, where aesthetics and verisimilitude are of paramount importance. In the context of real estate advertisements, overly-processed images are not well received by consumers, as they appear unreliable. For the consumer, it is important that the image faithfully represents the actual property. This, coupled with the intrinsic subjectivity of aesthetic image evaluation, makes image enhancement a difficult task to solve. Moreover, a single method is often not the best solution for a variety of cases; thus, a dynamic and automatic solution has been created to solve the problem. We presented the results of using Classical image processing filters, Pix2 Pix GANs, and a set of filters created using GP. We performed tests using state-of-the-art _no-reference_ IQA metrics: two metrics that analyzes the image quality (NIMA T. and BRISQUE), and two metrics that focus on image aesthetics (NIMA A and PHIL). The last one, qualifies an input image based on its appeal for online real estate image marketing. We present some analysis of the four metrics, including some examples of the use of this metrics with real estate images. The BRISQUE and PHIL IQA metrics present more spread-out range of scores. We also made use and one _full-reference_ method, SSIM, and MSE, as measures of similarity. As Table 3 shows, the results of the GANs follow the same trend as those obtained with the Classical and One-Click filters: the value of the aesthetic metrics (NIMA A and PHIL) is higher than that of the quality metrics (NIMA T and BRISQUE), although, with the GANs, the SSIM value increases, with a result closer to the original image. The best results for both quality and aesthetics metrics were achieved using GP. The GP system was designed using parameters that provided good results in previous work by the authors of References [9; 39; 40]. Three different fitness functions were designed for the GP system. The first (GP.A) uses a metric based on aesthetic value and provides images that improve this metric by 0.97. In fact, this enhancement is the largest achieved in all experiments in this study. However, the processing of these images is too strong, creating very interesting variations on the original images that may present artistic interest but are not suitable for presentation in real estate advertisements. The other two fitness functions are based on the sum of the values of two metrics, one being an aesthetics metric, and the other a quality or similarity metric. This results in more bounded processing suitable for use in real estate imaging. GP.B generates the highest increase in the PHIL metric, and GP.C results in images with high similarity to the original, presenting the highest SSIM of all experiments (SSIM = 0.88). On the other hand, we argue the importance of a threshold in IE solutions, as the results repeatedly showed that every image has an inherent maximum quality that was attempting to improve any further and will most of the time cause quality degradation. This came along with the conclusion that images in which the original quality is lower are generally easier to improve upon than those that demonstrate higher quality right away. We also developed a pipeline framework composed of Machine Learning methods, image processing filters, and an Evolutionary approach to create dynamic image enhancement pipelines capable of addressing this problem. IE is an extensive field; thus, it is virtually impossible to expand upon every detail of this work. The core of our solution relies on seven different elementary Classical functions. For future work, this set of functions can be altered in order to obtain different results. The parameters range for each function is also manually defined; thus, it could also be changed to allow the generated solutions to have more or less impact on the image.