Most methods of image quality assessment (QA) have been designed for QA of degraded images. This paper presents the results of a study designed to investigate whether existing QA methods can be adapted to succeed on enhanced images. We developed a database containing digitally enhanced images and associated subjective quality ratings. Next, we analyzed the efficacy of select QA methods and their reversemode versions in predicting the ratings. Given the fact that an enhanced image makes the original image appear degraded, we tested both normal and reverse-mode versions, where the latter were implemented by specifying the enhanced image as the reference and the original image as the "degraded" image. Our results demonstrate that this reverse-mode approach improves QA of enhanced images. We present a strategy for further improving the QA methods by using measures of contrast, sharpness, and color saturation.