LAUSR

Images captured in low-brightness environments often lead to poor visibility and exhibit artifacts such as low brightness, low contrast, and color distortion. These artifacts not only affect the visual perception of the human eye but also decrease the performance of computer vision algorithms. Existing deep learning-based image enhancements studies are quite slow and usually require extensive hardware specifications. Conversely, lightweight enhancement approaches do not provide satisfactory performance as compared to state-of-the-art methods. Therefore, we proposed a fast and lightweight deep learning-based algorithm for performing low-light image enhancement using the light channel of Hue Saturation Lightness (HSL). LiCENt stands for Light Channel Enhancement Network that uses a combination of an autoencoder and convolutional neural network (CNN) to train a low-light enhancer to first improve the illumination and later improve the details of the low-light image in a unified framework. This method used a single channel lightness ‘L’ of HSL color space instead of traditional RGB color channels which helps in reducing the number of learnable parameters by a factor of 8.92, at the most. LiCENt also has significant advantages for the Brilliance Perception Adjustment, which enables the model to avoid issues including over-enhancement and color distortion. The experimental results demonstrate that our approach generalizes well in synthetic and natural low-light images and outperforms other methods in terms of qualitative and quantitative metrics.