Optics and Precision Engineering, Volume. 32, Issue 12, 1915(2024)
Wavelet dehazeformer network for road traffic image dehazing method
Fig. 8. Shows the dehazing results of different methods. In (a1)-(a2), foggy images from the Foggy_Cityscapes dataset are presented. In (a3)-(a4), foggy images from the 4K-HAZE dataset are displayed. In (a5)-(a6), foggy images from the DKITTI dataset are depicted. Subfigures (b) through (i) represent dehazing results using various methods: (b) Dark Channel Prior, (c) AOD-Net, (d) Wavelet-Net, (e) FFA-Net, (f) EPDN, (g) DehazeFormer, (h) the proposed method, and (i) the ground truth image
Fig. 9. Enlarged View of the Red Region in the Fifth Dehazed Image of Figure 7; Among them, (ak) foggy image; (bk) the result of dark channel dehazing; (ck) the result of AOD-Net dehazing; (dk) the result of Wavelet-Net dehazing; (ek) the result of FFA-Net dehazing; (fk) the result of EPDN dehazing; (gk) the result of DehazeFormer dehazing; (hk) the result of the proposed method dehazing; (ik) the real image ; k=2,4,5
Fig. 10. Comparison of PSNR and SSIM for Different Dehazing Algorithms
|
|
|
|
Get Citation
Copy Citation Text
Ping XIA, Ziyi LI, Bangjun LEI, Yudie WANG, Tinglong TANG. Wavelet dehazeformer network for road traffic image dehazing method[J]. Optics and Precision Engineering, 2024, 32(12): 1915
Category:
Received: Mar. 5, 2024
Accepted: --
Published Online: Aug. 28, 2024
The Author Email: LEI Bangjun (Bangjun.Lei@ieee.org)