The human visual system possesses the remarkable ability to pick out salient objects in images. Even more impressive is its ability to do the very same in the presence of disturbances. In particular, the ability persists despite the presence of noise, poor weather, and other impediments to perfect vision. Meanwhile, noise can significantly degrade the accuracy of automated computational saliency detection algorithms. In this article, we set out to remedy this shortcoming. Existing computational saliency models generally assume that the given image is clean, and a fundamental and explicit treatment of saliency in noisy images is missing from the literature. Here we propose a novel and statistically sound method for estimating saliency based on a nonparametric regression framework and investigate the stability of saliency models for noisy images and analyze how state-of-the-art computational models respond to noisy visual stimuli. The proposed model of saliency at a pixel of interest is a data-dependent weighted average of dissimilarities between a center patch around that pixel and other patches. To further enhance the degree of accuracy in predicting the human fixations and of stability to noise, we incorporate a global and multiscale approach by extending the local analysis window to the entire input image, even further to multiple scaled copies of the image. Our method consistently outperforms six other state-of-the-art models (Bruce & Tsotsos, 2009; Garcia-Diaz, Fdez-Vidal, Pardo, & Dosil, 2012; Goferman, Zelnik-Manor, & Tal, 2010; Hou & Zhang, 2007; Seo & Milanfar, 2009; Zhang, Tong, & Marks, 2008) for both noise-free and noisy cases.