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Saliency computational model with active environment perception can be useful for many applications including image segmentation, image compression, image retrieval, and etc. Conventional saliency computational models rely on handcrafted low level features, such as color or contrast. These models face great difficulties in low lighting scenarios, due to the lack of well-defined feature to interpret saliency information in low contrast images. In this paper, a new approach is proposed to detect salient object from low contrast images. The proposed approach explores the most distinguishable salient information in low contrast images based on low level features. Extensive experiments have been conducted to evaluate the performance of the proposed method against the state-of-the-art saliency computational models.