Night Removal by Color Estimation and sparse representation

Night Removal is highly desired in both computational photography and computer vision applications. However, few works have been studied towards this goal. This paper proposes an effective algorithm for removing the night from a single input image. We present a new Color Estimation Model (CEM) for transforming the image from “night” to “day” - along with a guided statistical Dark-to-Day (D2D) prior directing for performance optimization. To restore the noisy and blurred image after CEM, sparse representation based on dozens of corresponding day-time images in different illuminations as dictionary training set is used in our algorithm. Extensive experiments on natural images show our algorithm can achieve convincing results.