FLIME—Fast Low Light Image Enhancement for Real-Time and Low-Compute Environments Using a Data-Centric Approach

Abstract

Low light image enhancement is a nontrivial task that finds application in autonomous driving, night vision devices for defence systems and other tasks that involve low light object detection. A majority of the existing solutions proposed, based on deep learning models, are resource intensive and take considerable time to process. Consequently, these are not suitable for processing a sequence of images for real time or even near-real-time video applications. This paper presents FLIME, a fast and efficient solution for the enhancement of low light images, which is amenable for use with a real-time video feed, and in low-compute environments because of its lightweight nature. FLIME is a pipeline of two steps: in the first step, a model is used to map input RGB values to output RGB values and, in the next step, contrast adjustment of the image is effected. The crux of the method comprises a linear transformation of the input RGB values. Since the processing uses a data-centric approach, a carefully curated dataset comprising images taken in low and bright light conditions is used in this study. Design considerations that underlie the preparation of the dataset are presented with the details of the proposed solution. Experimental results that include a qualitative comparison of the images enhanced by FLIME as well as a quantitative comparison of their PSNR, SSIM values and processing times against those of other enhancement methods on publicly available data for this purpose demonstrate the efficacy of the proposed solution.