# Towards Automatic Image Enhancement with Genetic Programming and Machine Learning
## 1 Introduction  
IE is an essential and ever-demanding branch of image processing and computer vision that allows visual improvement to images by manipulating their attributes. It can be used to alter an image in several different ways, for instance, by highlighting a specific feature to ease post-processing analyses by a human or a machine or by increasing its human perceived aesthetic. Each image has a set of attributes, such as size, color space, contrast, brightness, saturation, distortions, artifacts, noise, format, etc., that define how we, and different computer software solutions, perceive it. All these features are not isolated but, rather, interact with each other. The image format and compression, for instance, is pivotal to every other aspect of the image, as different formats allow for distinct color and compression properties that can, in turn, account for distinct visual characteristics. These attributes are often not well balanced between each other nor optimized for the image context and may cause a diverse range of deformations in the image quality. Moreover, IE is useful for different tasks and fields that benefit from pre-processing an image to improve human or machine perception. Such fields are medical imagery, space imagery, bio-metric, photography, and video editing, among others [1, 2, 3]. Aesthetic improvement via IE is also one of the most popular uses of these techniques. A great example of such use of IE is the post-processing that is applied to an image before introducing it in a context where image appeal is crucial, for instance, in advertisements. Today, many consumers choose their purchases via the Internet, with countless stimuli and options. On this basis, it is not surprising that more and more decisions are made based on the aesthetic value of products [4] and the images in their advertisements. From a commercial perspective, it is interesting to note that advertisers spend around US$ 13 billion annually on banner ads [5]. Intending to improve images' click-through rate (CTR), advertisers rely on image filter applications to adjust them. In this context, we decided to focus on a sector that advertises high economic value products and that the majority of the population consumes at some point in their lives: real estate. Currently, a large part of the real estate business is on the Internet, real estate portals, and the agencies' websites. In most cases, the first step to selling a real estate property, even before a visit, is to show pictures of it. If those photographs are not attractive to the client, it is probable that the visit is lost and, consequently, the sale itself. Recent advances in IE include image processing or Machine Learning approaches to solve specific problems [6]. Due to the vast set of applications and contexts, researchers rely on a set of image filters [7] or generative Machine Learning models, such as Generative Adversarial Networks (GAN) [8]. However, there is no simple solution that fits all IE problems. Specifically, real estate image marketing presents a complex IE scenario, where the overall aesthetics and technical aspects of the image must be adjusted to deliver a realistic, credible, and attractive result for customers. Real estate images are diverse in terms of content and the conditions in which they are taken (resolutions, perspectives, and lighting conditions, among others). Some photos are taken with professional-level cameras, while others are taken with inexpensive smartphones. There are also often indoor and outdoor photos, day and night, or there might even be a close-up of an ornament or a vase. Considering all of this, there is space for an automatic tool that combines state-of-the-art approaches and adapts dynamically to different input conditions, a requirement for the real estate marketing problem. The problem of IE can be formulated as a set of operations performed to the input image that can result in a better output from the perspective of qualitative or quantitative metrics that fulfill the objective.