  Numerous sand dust image enhancement algorithms have been proposed in recent
years. To our best acknowledge, however, most methods evaluated their
performance with no-reference way using few selected real-world images from
internet. It is unclear how to quantitatively analysis the performance of the
algorithms in a supervised way and how we could gauge the progress in the
field. Moreover, due to the absence of large-scale benchmark datasets, there
are no well-known reports of data-driven based method for sand dust image
enhancement up till now. To advance the development of deep learning-based
algorithms for sand dust image reconstruction, while enabling supervised
objective evaluation of algorithm performance. In this paper, we presented a
comprehensive perceptual study and analysis of real-world sand dust images,
then constructed a Sand-dust Image Reconstruction Benchmark (SIRB) for training
Convolutional Neural Networks (CNNs) and evaluating algorithms performance. In
addition, we adopted the existing image transformation neural network trained
on SIRB as baseline to illustrate the generalization of SIRB for training CNNs.
Finally, we conducted the qualitative and quantitative evaluation to
demonstrate the performance and limitations of the state-of-the-arts (SOTA),
which shed light on future research in sand dust image reconstruction.
