To address the poor background suppression and low foreground resolution in existing saliency detection methods, we propose a saliency detection algorithm based on background suppressing and foreground updating. First, the manifold ranking (MR) algorithm is used to calculate the background prior map, and the super-pixel segmentation algorithm for extracting edge super-pixels is used to construct a background template and calculate a sparse reconstruction map. Next, we obtain the high-quality suppressed background map through point multiplication. Subsequently, we use a Gaussian mixture model to calculate the color prior map, a CA (Cellular Automata) model to calculate the multiscale color optimization map, and point multiplication to obtain the high-precision updated foreground map. Finally, under the Bayesian framework, the suppressed background map and updated foreground map are fused to obtain the final saliency map that meets the requirements of the human eye. Experimental results on two public datasets show that the proposed algorithm can obtain a saliency map with good background suppression and high foreground resolution. Moreover, it provides improved precision, F-measure, mean absolute error, and other indicators relative to eight other algorithms used for comparison.