We propose to use gradient distribution specification for image enhancement. The specified gradient distribution is learned from natural-scene image datasets. This enhances image quality based on two facts: First, the specified distribution is independent of image content. Second, the distance between the learned distribution and the empirical distribution of a given image correlates with subjectively perceived image quality. Based on those two facts, remapping an image such that the distribution of its gradients (and therefore also Laplacians) matches the specified distribution is expected to improve the quality of that image. We call this process “image naturalization”. Our experiments confirm that naturalized images are more appealing to visual perception. Moreover, “naturalness” can be used as a measure of image quality when ground-truth is unknown.