Low-Light Image Enhancement by Principal Component Analysis

Under extreme low-lighting conditions, images have low contrast, low brightness, and high noise. In this paper, we propose a principal component analysis framework to enhance low-light-level images with decomposed luminance–chrominance components. A multi-scale retinex-based adaptive filter is developed for the luminance component to enhance contrast and brightness significantly. Noise is attenuated by a proposed collaborative filtering employed to both the luminance and chrominance components that reveal every finest detail by preserving the unique features in the image. To evaluate the effectiveness of the proposed algorithm, a simulation model is proposed to generate nighttime images for various levels of contrast and noise. The proposed algorithm can process a wide range of images without introducing ghosting and halo artifacts. The quantitative performance of the algorithm is measured in terms of both full-reference and blind performance metrics. It shows that the proposed method delivers state-of-the-art performance both in terms of objective criteria and visual quality compared to the existing methods.