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A Content-Based Automatic Filter Recommendationsystem for Photography and Image Editing using Artificial Intelligence

Authors

Yifan Luo1 and Yu Sun2, 1USA, 2California State Polytechnic University, USA

Abstract

This paper presents the development and evaluation of PhotoGleam, a mobile application designed to enhance image quality through AI-generated filters. The app addresses the common issue of aesthetically unappealing colors in mobile phone photography by leveraging advanced AI models to create custom filters tailored to each image. The paper details the implementation of the AI-driven image processing engine, the backend Flask server, and the Flutter frontend user interface. Through two key experiments, the effectiveness of AI-generated filters was assessed in terms of both image quality and user engagement. Results showed that users consistently preferred AI-generated filters over standard ones, and the introduction of these filters led to increased time spent on the app and more images edited. While there are areas for improvement, such as server scalability and AI model reliance, PhotoGleam demonstrates significant potential as a valuable tool for enhancing mobile photography.

Keywords

AI-generated filters, Mobile photography, Image enhancement, Photo editing app, Custom filters

Full Text  Volume 14, Number 19