Underwater Image Restoration Based on the Modified Model and Dark Channel Prior
LIN Sen1,2,3, BAI Ying1, LI Wentao2,3, TANG Yandong2,3
1. School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China; 2. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; 3. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
林森, 白莹, 李文涛, 唐延东. 基于修正模型与暗通道先验信息的水下图像复原[J]. 机器人, 2020, 42(4): 427-435,447.DOI: 10.13973/j.cnki.robot.190464.
LIN Sen, BAI Ying, LI Wentao, TANG Yandong. Underwater Image Restoration Based on the Modified Model and Dark Channel Prior. ROBOT, 2020, 42(4): 427-435,447. DOI: 10.13973/j.cnki.robot.190464.
Abstract：Due to the influence of complex imaging environment, there exist many problems in the underwater images acquired by optical vision system, such as low contrast, blur and color distortion. To solve this problem, an image restoration algorithm based on the modified scattering model is proposed. Firstly, the absorption attenuation characteristics of light in water are analyzed in depth, and based on the simplified atmospheric scattering model, the background light of water body is incorporated into the direct attenuation term of the model. Secondly, the inverse channel of red channel is used to compensate for the rapid attenuation of red light. Then, the background light value of water body is estimated by the hierarchical search algorithm based on quadtree. Finally, the medium transmittance is estimated on the basis of the modified imaging model by combining the underwater dark channel prior, and the underwater image is restored. The experimental results show that the restored underwater image displays natural color and can effectively restore the details of the far scene in image. The comprehensive evaluation index of image contrast, color and saturation is better than the contrast algorithm, and the proposed algorithm is suitable for different types of underwater degraded images.
[1] He K M, Sun J, Tang X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12):2341-2353. [2] Carlevaris-Bianco N, Mohan A, Eustice R M. Initial results in underwater single image dehazing[C]//OCEANS 2010. Piscataway, USA:IEEE, 2010. DOI:10.1109/OCEANS.2010. 5664428. [3] Drews P L J Jr, Nascimento E R, Botelho S S C, et al. Underwater depth estimation and image restoration based on single images[J]. IEEE Computer Graphics and Applications, 2016, 36(2):24-35. [4] Li C Y, Guo J C, Pang Y W. Single underwater image restoration by blue-green channels dehazing and red channel correction[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway, USA:IEEE, 2016:1731-1735. [5] 汤忠强,周波,戴先中,等.基于改进DCP算法的水下机器人视觉增强[J].机器人,2018,40(2):222-230. Tang Z Q, Zhou B, Dai X Z, et al. Underwater robot visual enhancements based on the improved DCP algorithm[J]. Robot, 2018, 40(2):222-230. [6] McGlamery B L. A computer model for underwater camera systems[C]//Proceedings of the SPIE, Vol.208. Bellingham, USA:SPIE, 1980:221-231. [7] Akkaynak D, Treibitz T. A revised underwater image formation model[C]//31st IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, USA:IEEE, 2018:6723-6732. [8] Zhang M H, Peng J H. Underwater image restoration based on a new underwater image formation model[J]. IEEE Access, 2018, 6:58634-58644. [9] Narasimhan S G, Nayar S K. Vision and the atmosphere[J]. International Journal of Computer Vision, 2002, 48(3):233-254. [10] Kim J H, Jang W D, Sim J Y, et al. Optimized contrast enhancement for real-time image and video dehazing[J]. Journal of Visual Communication and Image Representation, 2013, 24(3):410-425. [11] Li C Y, Guo J C, Cong R M, et al. Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior[J]. IEEE Transactions on Image Processing, 2016, 26(12):5664-5677. [12] Bianco G, Muzzupappa M, Bruno F, et al. A new color correction method for underwater imaging[J]. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015, 40(5):25-32. [13] Yang M, Sowmya A. An underwater color image quality evaluation metric[J]. IEEE Transactions on Image Processing, 2015, 24(12):6062-6071. [14] Berman D, Treibitz T, Avidan S. Non-local image dehazing[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, USA:IEEE, 2016:1674-1682. [15] Cai B L, Xu X M, Jia K, et al. DehazeNet:An end-to-end system for single image haze removal[J]. IEEE Transactions on Image Processing, 2016, 25(11):5187-5198. [16] Pan P W, Yuan F, Cheng E. Underwater image de-scattering and enhancing using DehazeNet and HWD[J]. Journal of Marine Science and Technology, 2018, 26(4):531-540. [17] 代成刚,林明星,王震,等.基于亮通道色彩补偿与融合的水下图像增强[J].光学学报, 2018, 38(11):78-87. Dai C G, Lin M X, Wang Z, et al. Color compensation based on bright channel and fusion for underwater image enhancement[J]. Acta Optica Sinica, 2018, 38(11):78-87.
