Evolving Image Enhancement Pipelines

摘要

Image enhancement is an image processing procedure in which the original information of the image is improved. It alters an image in several different ways, for instance, by highlighting a specific feature in order to ease post-processing analyses by a human or machine. In this work, we show our approach to image enhancement for digital real-estate-marketing. The aesthetic quality of the images for real-estate marketing is critical since it is the only input clients have once browsing for options. Thus, improving and ensuring the aesthetic quality of the images is crucial for marketing success. The problem is that each set of images, even for the same real-estate item, is often taken under diverse conditions making it hard to find one solution that fits all. State of the art image enhancement pipelines applies a set of filters that solve specific issues, so it is still hard to generalise that solves all types of issues encountered. With this in mind, we propose a Genetic Programming approach for the evolution of image enhancement pipelines, based on image filters from the literature. We report a set of experiments in image enhancement of real state images and analysed the results. The overall results suggest that it is possible to attain suitable pipelines that visually enhance the image and according to a set of image quality assessment metrics. The evolved pipelines show improvements across the validation metrics, showing that it is possible to create image enhancement pipelines automatically. Moreover, during the experiments, some of the created pipelines create non-photorealistic rendering effects in a moment of computational serendipity. Thus, we further analysed the different evolved non-photorealistic solutions, showing the potential of applying the evolved pipelines in other types of images.