Data-driven Approach to Aesthetic Enhancement

Abstract

Traditional image enhancement techniques revise the distribution of pixels or local structure and achieve the impressive performance in image denoising, contrast enhancement and color adjustment. However, they are not effective to improve the overall aesthetic image quality because it may involve contextual modifications, including the removal of disturbing objects, inclusion of appealing visual elements or relocation of the target object. In this paper, we propose a new aesthetic enhancement technique that edits the structural image element guided by a large collection of good exemplars. More specifically, we remove/insert image elements and resize/relocate objects based on good exemplars. Additionally, we remove undesirable regions determined by user interaction and fill these holes seamlessly guided by the exemplars. Based on the experimental evaluation on the database of two landmarks, we observe the considerable improvement in aesthetic quality.