  In this work, we investigate the possibility of replacing the $\ell_2$ loss
with perceptually derived loss functions (SSIM, MS-SSIM, etc.) in training an
end-to-end dehazing neural network. Objective experimental results suggest that
by merely changing the loss function we can obtain significantly higher PSNR
and SSIM scores on the SOTS set in the RESIDE dataset, compared with a
state-of-the-art end-to-end dehazing neural network (AOD-Net) that uses the
$\ell_2$ loss. The best PSNR we obtained was 23.50 (4.2% relative improvement),
and the best SSIM we obtained was 0.8747 (2.3% relative improvement.)
