Opinion-unaware no-reference (OU-NR) methods for image quality assessment (IQA) are of great interest since they can predict visual quality independent of a reference image and knowledge of human quality opinions. Models of image naturalness trained on a corpus of pristine images have shown potential for developing OU-NR methods. However, the extracted features may not match the preferences of the human visual system (HVS). This paper aims to utilize the features of convolutional neural networks to achieve a richer representation of the naturalness space. In addition, the IQA processing steps from training to quality measurement are revisited and the naturalness model is improved by incorporating HVSinspired criteria. Experimental results show the higher performance and generalizability of the naturalness model – constructed using HVS-aligned deep features – under different distortion types and image contents. The source code of the quality index is available at https://gitlab.com/saeedmp/dni.