  Ambient lighting conditions play a crucial role in determining the perceptual
quality of images from photographic devices. In general, inadequate
transmission light and undesired atmospheric conditions jointly degrade the
image quality. If we know the desired ambient factors associated with the given
low-light image, we can recover the enhanced image easily \cite{b1}. Typical
deep networks perform enhancement mappings without investigating the light
distribution and color formulation properties. This leads to a lack of image
instance-adaptive performance in practice. On the other hand, physical
model-driven schemes suffer from the need for inherent decompositions and
multiple objective minimizations. Moreover, the above approaches are rarely
data efficient or free of postprediction tuning. Influenced by the above
issues, this study presents a semisupervised training method using no-reference
image quality metrics for low-light image restoration. We incorporate the
classical haze distribution model \cite{b2} to explore the physical properties
of the given image in order to learn the effect of atmospheric components and
minimize a single objective for restoration. We validate the performance of our
network for six widely used low-light datasets. The experiments show that the
proposed study achieves state-of-the-art or comparable performance.
