## 5 Experiments
### Subjective Evaluation  
This subsection aims to evaluate the proposed algorithm by first comparing the performance of color correction. Figure 4 shows six sandstorm images with significant color shifts and the corresponding color-corrected versions.  
As shown in Figure 4, the Gray World [42] and Shades of Gray [43] algorithms fail to correct severe redshifts. Their incorrect gain coefficients cause the corrected images to have a blue or yellow tint. SCB [5] and CVC [12] achieve commendable results but fail to effectively suppress the red channel when the intensity of the green channel is low, causing red artifacts in specific regions. In contrast, our method successfully avoids these issues, significantly restoring the original image colors. Note that Figure 4 only shows the color correction results as a reference, with the full results provided later in the text.  
To further evaluate the dust removal performance of our algorithm, 18 representative sandstorm images are selected for comparison, as shown in Figures 5-7. In mild sandstorm scenarios, most methods are unaffected by color shifts, except HRDCP [11]. Despite showing commendable dust removal capabilities, these traditional methods still struggle to restore distant buildings. DedustGAN [10] introduces color distortion in some cases. In contrast, our algorithm showcases robust sand dust removal performance, almost eliminating dust in the near field and markedly improving the visibility for distant objects, as shown in Figure 5.  
Figure 6 illustrates the experimental outcomes for yellow sandstorm images. The FBE [3] method efficiently removes sand dust, although the enhanced images show localized dark areas. HRDCP [11] significantly enhances the contrast but at the expense of introducing noticeable color distortions. The results from NGT [4] exhibit a grayish-green tint. Lacking a color correction preprocessing step, ROP [13] and ROP+ [14] have a prominent yellow tint in the results. SCB [5] delivers esthetically pleasing results, although some of the results display an unnatural color tone. CVC [12] restores the color effectively but results in an overall darkened image. While DedustGAN [10] successfully restores more details, it  
Figure 4: Comparison of color correction results.  
introduces noticeable noise and blurriness. In contrast, our algorithm accurately recovers the original colors and maintains proper brightness.  
Figure 7 presents the experimental results for red sandstorm images. While FBE [3] effectively mitigates the red veil in the sandstorm images, the results exhibit a yellowish hue. The results of HRDCP [11] and NGT [4] exhibit an orange tint. Due to limited color correction, ROP [13] and ROP+ [14] show significant color cast residues and purple artifacts in the restored results. Although SCB [5] and CVC [12] perform well in color correction, they still leave red artifacts in local areas, and the sky appears bluish. DedustGAN [10] produces results with severe color distortion and purple artifacts.  
Figure 5: Comparison of recovery results of weak sandstorm images.  
Figure 6: Comparison of recovery results of yellow sandstorm images.  
Figure 7: Comparison of recovery results of severe sandstorm images.  
effectively balances color correction and dust removal, successfully eliminating the red veil while ensuring comprehensive dust removal from the sandstorm images.  
To further demonstrate the robustness of the algorithm, restoration results for 12 additional sandstorm images are provided in Figure 8. As shown in Figure 8, the proposed method is highly effective in correcting color biases, restoring fine details, and minimizing color artifacts.