Previous research has suggested only weak statistical dependencies between local luminance and contrast in natural images. Here we study luminance and contrast in natural images using established measures and show that when multiple measurements of these two local quantities are taken in different spatial locations across the visual field, strong dependencies are revealed that were not apparent in previous pointwise (single-site) analyses. We present a few simple experiments demonstrating this spatial dependency of luminance and contrast and show that the luminance measurements can be used to approximate the contrast measurements. We also show that relying on higher-order statistics, independent component analysis learns paired spatial features for luminance and contrast. These features are shown to share orientation and localization, with the filters corresponding to the features dependent in their outputs. Finally, we demonstrate that the found dependencies also exist in artificial images generated from a dead leaves model, implying that simple image phenomena may suffice to account for the dependencies. Our results indicate that local luminance and contrast computations do not recover independent information sources from the visual signal. Subsequently, our results predict spatial processing of local luminance and contrast to be non-separable in visual systems.