  The quality assessment (QA) of restored low light images is an important tool
for benchmarking and improving low light restoration (LLR) algorithms. While
several LLR algorithms exist, the subjective perception of the restored images
has been much less studied. Challenges in capturing aligned low light and
well-lit image pairs and collecting a large number of human opinion scores of
quality for training, warrant the design of unsupervised (or opinion unaware)
no-reference (NR) QA methods. This work studies the subjective perception of
low light restored images and their unsupervised NR QA. Our contributions are
two-fold. We first create a dataset of restored low light images using various
LLR methods, conduct a subjective QA study and benchmark the performance of
existing QA methods. We then present a self-supervised contrastive learning
technique to extract distortion aware features from the restored low light
images. We show that these features can be effectively used to build an opinion
unaware image quality analyzer. Detailed experiments reveal that our
unsupervised NR QA model achieves state-of-the-art performance among all such
quality measures for low light restored images.
