Multiscale Reflection Component Based Weakly Illuminated Nighttime Image Enhancement

In this work, a novel multiscale reflection component-based enhancement algorithm is proposed for the nighttime input image. This model not only recovers the contrast of an image but also highlights the hidden details in input while conserving natural color in an image. The proposed method exploits the multiscale Gaussian function to evaluate the illumination layer of the image. Based on Weber Fechner's law, an image brightness improvement scheme is proposed which adaptively controls the constraint of the enhancement function and hence, features of an image can be enhanced globally. Furthermore, the principal component analysis (PCA) based, image fusion method is designed to extract significant information from the multiple images of the same scene. The PCA can efficiently blend multiple images of same image to extract desirable features with more details. Finally, the local contrast of an image is improved by an application of the contrast-limited adaptive histogram equalization (CLAHE) technique. The experimental fallouts advocate the efficacy of the proposed algorithm over other methods. On subjective and objective analyses, it is observed that the proposed method outperforms when it is compared with several states of the arts.