Laser & Optoelectronics Progress, Volume. 59, Issue 22, 2215004(2022)
Underwater Image Enhancement Based on Dense Cascaded Convolutional Neural Network
Fig. 4. Structure of texture refinement. (a) Texture refinement network; (b) texture refinement unit
Fig. 5. Degraded underwater image, clear underwater image corresponding to degraded underwater image, HSV color space image corresponding to degraded underwater image and its component images. (a) Degraded images, (b) corresponding clear underwater images, (c) corresponding HSV color space images, (d) corresponding H component images, (e) corresponding S component images, and (f) corresponding V component images of coral and whale skeleton
Fig. 7. Experimental results of different algorithms. (a) Original images; (b) CLAHE; (c) UDCP; (d) FE; (e) CycleGAN; (f) WSCT; (g) prposed algorithm
Fig. 8. Diagram of different models. (a) model2; (b) model3; (c) model4; (d) model5
Fig. 9. Different model's results. (a) model2's result; (b) model3's result; (c) model4's result; (d) model5's result; (e) proposed model's result
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Qingjiang Chen, Yali Xie. Underwater Image Enhancement Based on Dense Cascaded Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2215004
Category: Machine Vision
Received: Jul. 26, 2021
Accepted: Oct. 13, 2021
Published Online: Oct. 12, 2022
The Author Email: Yali Xie (2696453994@qq.com)