基于光学成像模型的水下图像超分辨率重构

doi:10.3969/j.issn.1006-2475.2017.04.002

计算机与现代化

基于光学成像模型的水下图像超分辨率重构

1.河海大学物联网工程学院,江苏常州213022；2.河海大学江苏省输配电装备技术重点实验室,江苏常州213022；
3.江苏省“世界水谷”与水生态文明协同创新中心,江苏南京211100

收稿日期:2016-10-24 出版日期:2017-04-20 发布日期:2017-05-08
作者简介:张颢(1991-),男,江苏常州人,河海大学物联网工程学院和河海大学江苏省输配电装备技术重点实验室硕士研究生,研究方向:视觉仿生及数字图像处理; 范新南(1965-),男,江苏宜兴人,教授,博士生导师,博士,研究方向:信息获取与处理,智能感知与信息处理,传感网理论与应用; 李敏(1982-),女,山西大同人,副教授,博士,研究方向:图像信号获取与处理； 张学武(1973-),男,陕西通渭人,教授,博士,研究方向:智能信息处理理论与技术,检测技术与智能系统,传感网理论与应用。
基金资助:
国家自然科学基金资助项目(61573128，61273170)；国家重点研发计划项目(2016YFC0401606); 中央高校基本科研业务费专项资金项目(2015B25214)

Underwater Image Super-resolution Reconstruction Based on Optical Imaging Model

1. College of Internet of Things Engineering, Hohai University, Changzhou 213022, China;
2. Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, China;
3. Jiangsu Provincial Collaborative Innovation Center of World Water Valley and Water Ecological Civilization, Nanjing 211100, China

Received:2016-10-24 Online:2017-04-20 Published:2017-05-08

摘要/Abstract

摘要： 目前大多数超分辨率重构算法都是针对空域图像进行恢复。考虑到水下光学条件复杂，直接把现有超分辨率重构算法移植到水下，无法对光线在水中传播产生的散射和衰减进行有针对性的处理。基于此，本文结合不同成像模型，提出一种基于光学成像模型的水下图像超分辨率重构算法。首先，考虑到水中光线散射对图像降质严重，根据水下光学成像模型，利用暗通道原理估算观测数据中的散射光和光线透射图，作为噪声估计的结果；然后对去除散射光的观测数据进行凸集投影超分辨率重构，得到高分辨率图像；最后利用透射图对高分辨率图像进行光线衰减补偿，去除由于水对光线的吸收作用造成的图像亮度降低和模糊，得到最终的恢复图像。对本文算法进行仿真，并与经典的超分辨率重构算法比较，验证了本文算法对恢复图像质量的提高作用。
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关键词: 水下图像, 超分辨率重构, 光学成像模型, 暗通道原理

Abstract: Now most super-resolution reconstruction algorithms are applied to atmospheric picture restoration. Taking the complexity of underwater optical environment into consideration, it is hard to perform direct processes towards scattering and attenuation caused by water simply by transplanting those super-resolution reconstruction algorithms to underwater images. In such condition, an underwater image super-resolution reconstruction algorithm based on optical imaging model is proposed by integrating different imaging models. Firstly, aiming at the severe degradation in image quality caused by light scattering in water, dark channel prior is used, based on underwater optical imaging model, to estimate scattered light and transmission in observed data to produce the result of noise estimation. Secondly, super-resolution reconstruction of projection onto convex sets is performed on the low-resolution image sequence from which scattered light is removed in order to produce a high-resolution image. At last, aiming at overcoming the decrease in intensity and blur caused by water so as to produce the restored image, light compensation is conducted on the high-resolution image using transmission. By comparing the reconstructed images produced by the proposed algorithm with those produced by classical super-resolution algorithms, quality improvement in restored images by our algorithm is proved in algorithm simulation.
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Key words: underwater image, super-resolution reconstruction, optical imaging model, dark channel prior

中图分类号:

TP391.4

张颢1,2,范新南1,2,3,李敏1,3,张学武1,3. 基于光学成像模型的水下图像超分辨率重构[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2017.04.002.

ZHANG Hao1,2, FAN Xin-nan1,2,3, LI Min1,3, ZHANG Xue-wu1,3. Underwater Image Super-resolution Reconstruction Based on Optical Imaging Model[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2017.04.002.

参考文献

［1］ Schettini R, Corchs S. Underwater image processing: State of the art of restoration and image enhancement methods［J］. EURASIP Journal on Advances in Signal Processing, 2010,2010(1): Article ID.746052.
［2］赵欣慰,金韬,池灏,等. 不同光照条件下水下成像背景光的建模与研究［J］. 物理学报, 2015,64(10):10420-1-10420-8.
［3］龙超. 图像超分辨率重建算法综述［J］. 科技视界, 2015(13):88-89.
［4］ Tsai R Y, Huang T S. Multiframe image restoration and registration［J］. Advances in Computer Vision and Image Processing, 1984,1(2):317-339.
［5］ Purkait P, Chanda B. Super resolution image reconstruction through Bregman iteration using morphologic regularization［J］. IEEE Transactions on Image Processing, 2012,21(9):4029-4039.
［6］ Camponez M O, Salles E O T, Sarcinelli-Filho M. Super-resolution image reconstruction using non-parametric Bayesian channel prior［C］// Conference on Computer Vision and Pattern Recognition. 2011:2341-2353.
［7］ Wang Lingfeng, Xiang Shiming, Meng Gaofeng, et al. Edge-directed single-image super-resolution via adaptive gradient magnitude self-interpolation［J］. IEEE Transactions on Circuits & Systems for Video Technology, 2013,23(8):1289-1299.
［8］ Yuan Qiangqiang, Zhang Liangpei, Shen Huanfeng. Multiframe super-resolution employing a spatially weighted total variation model［J］. IEEE Transactions on Circuits & Systems for Video Technology, 2012,22(3):379-392.
［9］ Kim Y, Han O, Bilgin A. Super resolution reconstruction based on block matching and three-dimensional filtering with sharpening［J］. IET Image Processing, 2015,9(12):1048-1056.
［10］Liang Meiyu, Du Junping, Cao Shouxin, et al. Super-resolution reconstruction based on multisource bidirectional similarity and non-local similarity matching［J］. IET Image Processing, 2015,9(11):931-942.
［11］Chiang J Y, Chen Y C. Underwater image enhancement by wavelength compensation and dehazing［J］. IEEE Transactions on Image Processing, 2012,21(4):1756-1769.
［12］Serikawa S, Lu Huimin. Underwater image dehazing using joint trilateral filter［J］. Computers & Electrical Engineering, 2014,40(1):41-50.
［13］Ghani A S A, Isa N A M. Underwater image quality enhancement through integrated color model with Rayleigh distribution［J］. Applied Soft Computing, 2015,27:219-230.
［14］He Kaiming, Sun Jian, Tang Xiaoou. Single image haze removal using dark channel prior［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011,33(12):2341-2353.
［15］Schechner Y Y, Averbuch Y. Regularized image recovery in scattering media［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007,29(9):1655-1660.
［16］Gordon H R. Dependence of the diffuse reflectance of natural waters on the sun angle［J］. Limnology & Oceanography, 1989,34(8):1484-1489.
［17］Elad M, Feuer A. Restoration of a single super-resolution image from several blurred, noisy, and undersampled measured images［J］. IEEE Transactions on Image Processing, 1997,6(12):1646-1658.
［18］陈伟,范新南,李敏,等. 单幅图像去雾算法［J］. 计算机与现代化, 2015(5):40-47.
［19］Stark H, Oskoui P. High-resolution image recovery from image-plane arrays, using convex projections［J］. Journal of the Optical Society of America A Optics & Image Science, 1989,6(11):1715-1726.

基于光学成像模型的水下图像超分辨率重构

Underwater Image Super-resolution Reconstruction Based on Optical Imaging Model

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