The presence of fog in an image reduces contrast which can be considered a nuisance in imaging applications, however, we consider this useful information for image enhancement and scene understanding. In this work, we present a new method for estimating depth from fog in a single image and single image fog removal. We use an example based approach that is trained from data with known fog and depth. A data driven method and physics based model are used to develop the example based learning framework for single image fog removal. In addition, we account for various colors of fog by using a linear transformation of the RGB colorspace. This approach has the flexibility to learn from various scenes and relaxes the common constraint of fixed camera position. We present depth estimations and fog removal from a single image with good results.