Style-Driven Image Enhancement for Entry-Level Mobile Devices
Angelo Christian Matias, Neil Patrick Del Gallego · 2024
Modern smartphones usually have automatic camera adjustment features that predetermine how images will be processed. Without an intervention from the user (e.g., manual adjustment of exposure settings, addition/removal of certain image filters), the predetermined camera settings dictate the look and feel of images taken. Since higher-end mobile devices tend to gravitate towards a more visually appealing style and clearer images, image enhancement on entry-level devices could be performed by transferring the style from a higher-end device to a lower-end one. This paper proposes a learning-based, style-driven image enhancement for entry-level devices. Using a deep residual style transfer network, we train a model that learns the relationship between images taken from a high-end device and those taken from an entry-level device to create a filter that could be used to enhance the images captured from an entry-level device. Our quantitative and qualitative analyses show that our proposed method can enhance images to match the qualities produced by higher-end mobile device cameras.