Laser & Optoelectronics Progress, Volume. 60, Issue 4, 0401003(2023)
Underwater Image Restoration Based on Classification and Dark Channel Prior with Minimum Convolutional Area
Fig. 2. Flowchart of HSV-CIELAB classification equalization and minimum convolution area DCP
Fig. 3. CIELAB color equalization results. (a) RGB channel grayscale of raw image; (b) RGB channel grayscale of balanced image
Fig. 4. Comparison of LAB histograms of images. (a) Channel L of raw image; (b) channel a of raw image; (c) channel b of raw image; (d) channel L of balanced image; (e) channel a of balanced image; (f) channel b of balanced image
Fig. 6. Backlight estimation. (a) Original image; (b) dark channel
Fig. 7. Example of backlight estimation with minimum convolution area DCP algorithm
Fig. 8. Comparison of visual results of different algorithms. (a) (b) High-saturation distorted images; (c) (d) low-saturation distortion images; (e) (f) shallow water images
Fig. 9. Enhancement results of different algorithms and comparison with reference images. (a) (b) (c) High-saturation distorted images; (d) (e) (f) (g) (h) low-saturation distorted images; (i) shallow water image
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Guodong Liu, Lihui Feng, Jihua Lu, Jianmin Cui. Underwater Image Restoration Based on Classification and Dark Channel Prior with Minimum Convolutional Area[J]. Laser & Optoelectronics Progress, 2023, 60(4): 0401003
Category: Atmospheric Optics and Oceanic Optics
Received: Jan. 27, 2022
Accepted: Mar. 30, 2022
Published Online: Feb. 14, 2023
The Author Email: Lihui Feng (lihui.feng@bit.edu.cn), Jihua Lu (lujihua@bit.edu.cn)