Humans have a natural tendency to view a visually attractive (i.e., salient) object in its entirety. However, previous methods for salient object detection only highlight some parts of the salient object. This problem severely limits the adoption of such technologies to various computer vision and pattern recognition applications. To address the problem, in this paper, we present a novel framework to improve a saliency map obtained from recent state-of-the-art salient object detection approaches. Based on the fact that the L0 optimization can efficiently minimize variation between values, we integrate a background saliency and an initial saliency based on the nonlocal L0 optimization. In our work, we first extract background samples to estimate the background saliency building upon the initial saliency and color information. We then integrate the background saliency into the initial saliency by solving an optimization problem. We formulate the optimization problem based on the nonlocal L0 gradient to efficiently minimize the saliency variation in the salient object. To confirm the effectiveness of our proposed method, we apply the proposed framework to the saliency maps generated from state-of-the-art methods. Experimental results on benchmark datasets demonstrate that the proposed framework significantly improves the saliency maps. Furthermore, we compare the performance of two refinement frameworks and ours to prove the superiority of our work.