## 5 Experiments
### Objective Evaluation  
To objectively assess the performance of our algorithm, we employed five widely used no-reference image quality evaluation metrics, including the rate of new visible edges e [44], the geometric mean of visibility level enhancement \(\bar{r}\)[44], the natural image quality evaluator (NIQE) [45], the natural scene statistics and perceptual characteristics-based quality index (NPQI) [46], and the structure, naturalness, and perception-driven NIQE (SNP-NIQE) [47]. Theoretically, a higher e value indicates richer detail in the restored result; a higher \(\bar{r}\) value signifies a more pronounced enhancement in contrast in the sandstorm images by the algorithm. The NIQE, NPQI, and SNP-NIQE evaluate the image quality based on statistical features, with lower scores indicating better image quality. We initially performed a quantitative comparison using the 18 sandstorm images displayed. Table 1 presents the average values of these metrics, with the best results highlighted in red, the second-best in green, and the third in blue.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & \(e\uparrow\) & \(\bar{r}\uparrow\) & **NIQE \(\downarrow\)** & **NPQI \(\downarrow\)** &
\begin{tabular}{c} **SNP-** \\ **NIQE \(\downarrow\)** \\ \end{tabular} \\ \hline FBE [3] & 0.8905 & 2.7882 & 3.4897 & 10.2192 & 5.8600 \\ HRDCP [11] & 0.7931 & 4.5853 & 3.7228 & 10.5894 & 5.9373 \\ NGT [4] & 0.4157 & 1.9198 & 3.2431 & 9.9789 & 5.5269 \\ ROP [12] & 0.4558 & 2.0696 & 3.6074 & 10.7137 & 5.5782 \\ SCB [5] & 0.7754 & 2.4037 & 3.4271 & 10.0767 & 5.3070 \\ ROP+ [13] & 1.1099 & 3.5369 & 3.6466 & 10.1720 & 5.9190 \\ CVC [14] & 0.6639 & 1.7882 & 3.5646 & 11.1459 & 5.5255 \\ DedustGAN [10] & 1.4277 & 3.5972 & 3.6165 & 9.5147 & 4.9324 \\ Ours & 1.0510 & 3.0082 & 3.4204 & 9.7736 & 5.3356 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average no-reference image quality metrics of Figures 5–7.  
Figure 8: More experimental results for the proposed method: (**a**) Restoration results of yellow sandstorm images; (**b**) Restoration results of red sandstorm images.  
As shown in Table 1, HRDCP [11] achieves the most noticeable improvement in the contrast of sand-dust images, while NGT [4] performs best in preserving their natural characteristics. However, both methods demonstrate limited capabilities in edge restoration and fail to recover the true colors of sand-dust images effectively. DedustGAN [10], employing a reference-free image fusion strategy, achieves notable advantages in the quantitative metrics. Nevertheless, while the fusion strategy leverages the strengths of dual-branch networks, it also inherits their weaknesses, resulting in poor visual quality. Although the proposed method does not outperform other methods in the evaluation metrics, it ranks consistently high across all evaluation criteria. When integrating subjective and objective evaluations, our algorithm demonstrates robust restoration performance for sand-dust images across diverse scenarios.  
To further confirm the effectiveness of the proposed algorithm in sandstorm removal, we conduct quantitative experiments on a dataset of 500 real-world sandstorm images, with the results summarized in Table 2. The proposed algorithm achieves the best performance in e, NIQE, and NPQI and ranks second in SNP-NIQE, demonstrating its effectiveness in enhancing edge information while significantly improving the overall image quality. However, the less favorable result for \(\bar{r}\) indicates a decline in performance due to artifact removal. This compromise arises because color distortion and artifacts, while undesirable, still align with the solution space and contribute positively to the evaluation metrics. To conclude, the proposed sand-dust image restoration method effectively balances the challenges of color correction and dust removal, yielding visually satisfying outcomes in most cases. Moreover, it demonstrates performance on par with that of other methods in terms of objective evaluation metrics.