Abstract
Image Enhancement (IE) is an image processing procedure in which the image’s original information is improved, highlighting specific features to ease post-processing analyses by a human or machine. State-of-the-art image enhancement pipelines apply solutions to fixed and static constraints to solve specific issues in isolation. In this work, an IE system for image marketing is proposed, more precisely, real estate marketing, where the objective is to enhance the commercial appeal of the images, while maintaining a level of realism and similarity with the original image. This work proposes a generic image enhancement pipeline that combines state-of-the-art image processing filters, Machine Learning methods, and Evolutionary approaches, such as Genetic Programming (GP), to create a dynamic framework for Image Enhancement. The GP-based system is trained to optimize 4 metrics: Neural Image Assessment (NIMA) technical and BRISQUE, which evaluate the technical quality of the images; and NIMA aesthetics and PhotoILike, that evaluate the commercial attractiveness. It is shown that the GP model was able to find the best image quality enhancement (0.97 NIMA Aesthetics), while maintaining a high level of similarity with the original images (Structural Similarity Index Measure (SSIM) of 0.88). The framework has better performance according to the image quality metrics than the off-the-shelf image enhancement tool and the framework’s isolated parts.
Highlights
Image Enhancement (IE) is an essential and ever-demanding branch of image processing and computer vision that allows visual improvement to images by manipulating their attributes
A conclusion from the previous work was that making the objective only about aesthetics could sometimes yield overly transformed images
To have a criterion for all the future experiments, we measured the improvement achieved by One-Click, the external IE software, on the test dataset using all available metrics
Summary
IE is an essential and ever-demanding branch of image processing and computer vision that allows visual improvement to images by manipulating their attributes. 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
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