Laser & Optoelectronics Progress, Volume. 60, Issue 4, 0401003(2023)
Underwater Image Restoration Based on Classification and Dark Channel Prior with Minimum Convolutional Area
Fig. 1. Schematic diagram of underwater formation model
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. 5. Composite false color 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: Feng Lihui (lihui.feng@bit.edu.cn), Lu Jihua (lujihua@bit.edu.cn)