Real-time low-light video enhancement on smartphones

Abstract

AbstractReal-time low-light video enhancement on smartphones remains an open challenge due to hardware constraints such as limited sensor size and processing power. While night mode cameras have been introduced in smartphones to acquire high-quality images in light-constrained environments, their usability is restricted to static scenes as the camera must remain stationary for an extended period to leverage long exposure times or burst imaging techniques. Concurrently, significant process has been made in low-light enhancement on images coming out from the camera’s image signal processor (ISP), particularly through neural networks. These methods do not improve the image capture process itself; instead, they function as post-processing techniques to enhance the perceptual brightness and quality of captured imagery for display to human viewers. However, most neural networks are computationally intensive, making their mobile deployment either impractical or requiring considerable engineering efforts. This paper introduces VLight, a novel single-parameter low-light enhancement algorithm that enables real-time video enhancement on smartphones, along with real-time adaptation to changing lighting conditions and user-friendly fine-tuning. Operating as a custom brightness-booster on digital images, VLight provides real-time and device-agnostic enhancement directly on users’ devices. Notably, it delivers real-time low-light enhancement at up to 67 frames per second (FPS) for 4K videos locally on the smartphone.