A comprehensive benchmark analysis for sand dust image reconstruction

TL;DR

This paper introduces a large-scale benchmark dataset for sand dust image reconstruction, enabling supervised evaluation of algorithms and advancing deep learning methods in this field.

Contribution

It presents the first comprehensive benchmark dataset (SIRB) for sand dust image reconstruction, facilitating supervised training and performance evaluation of CNN-based algorithms.

Findings

SIRB enables quantitative assessment of reconstruction algorithms.

Deep learning models trained on SIRB outperform traditional methods.

The study highlights current limitations and future directions in sand dust image enhancement.

Abstract

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.