摘要
针对水下图像普遍存在低对比度、低亮度和颜色失真,以及现有的水下图像复原方法恢复结果不自然、亮度不均和主体色调偏红等问题,该研究提出了双背景光自适应融合与透射图精准估计水下图像方法。采用基于水下光衰减特性和背景光平坦性的双背景光自适应融合策略以提高估算的融合背景光准确度,通过新型水下暗通道先验、反向饱和图和三通道光谱衰减系数估算出更加精准的水下图像透射图,最后将估算出的融合背景光与精准透射图应用于水下成像模型得到复原后的水下图像。在广东罗非鱼良种场水产养殖数据集和水下图像增强基准数据集的试验结果表明:对比暗通道先验、最大强度先验、基于模糊和光吸收、蓝绿通道去雾、基于背景光统计模型和透射图优化5种水下图像复原方法,在主观恢复效果评价中,该文方法能有效纠正水下图像失真、亮度偏暗和主体色调偏红等问题;在7个客观评价指标中,该文方法在6个指标中取得最好值,其中全参考图像质量评价指标中的峰值信噪比、结构相似性、均方误差和视觉信息保真度等数值比次好水下图像复原方法分别提升了0.52%、2.1%、3.4%和0.86%;无参考图像质量评价指标中的自然图像质量评价指标和水下图像质量评价数值比次好水下图像复原方法分别提升了2.4%和7.4%。该文方法在解决传统水下图像复原方法中存在的亮度不均和颜色偏红等问题具有一定优势,可以为水下图像复原方法提供技术借鉴。
Underwater images have been highly required in many application scenarios, such as marine resources exploration,reservoir safety evaluation, and underwater aquaculture monitoring. But the captured images under water are usually degraded,due to the refraction, absorption, and scattering of light by the suspended particles in water. Some limitations of images can be low contrast, blurred details, and color distortion. Therefore, it is urgent for underwater image restoration. Much effort has been made on a variety of underwater image restoration using processing technology so far. However, land image restoration cannot be simply transformed the underwater images using background light estimation. The best restoration cannot be achieved in the current case, such as unnatural restoration, uneven brightness, and reddening color tone of the main body. In this study, an underwater image restoration was proposed using the adaptive fusion of double background lights and accurate estimation of the transmission map. First, two kinds of background lights of the underwater images were obtained using the maximum intensity prior background light estimation method based on underwater light attenuation and Quadtree background light estimation method based on background light flatness. Then, an adaptive fusion algorithm of double background lights was created to realize the accurate estimation of background lights in different scenes. Second, an accurate estimation of the transmission map was established to combine the new underwater dark channel prior, Reversed Saturation Map, and three-channel spectral attenuation coefficient. Furthermore, the restored underwater image was constructed using the obtained fused background light and accurate transmission map. The brightness of the image was adjusted to obtain the final underwater restored image. Finally, the experiment was carried out to verify the model using the aquaculture dataset of Guangdong Tilapia Breeding Farm and Underwater Image Enhancement Benchmark Dataset. The result showed that the improved estimation was better adapted to different underwater environments, thereby improving the contrast and color of the restored underwater image. A better restoration was achieved than the rest, such as dark channel prior, the maximum intensity prior, restoration using blur and light absorption, restoration using blue-green channel defogging, restoration using background light statistics model, and optimization of transmission map. Furthermore, the restoration was evaluated to deal with the current underwater image distortion, dim brightness, and reddish tone. The best value was also achieved among the seven objective evaluation indexes. The Peak Signal to Noise Ratio, Structural Similarity, Mean-Square Error, and Visual Information Fidelity increased by 0.52%, 2.1%, 3.4%, and 0.86%, respectively, in the evaluation indexes of reference image quality using the best underwater image restoration, compared with the second best. Correspondingly, the Natural Image Quality Evaluator, and Natural Image Quality Measure index were improved by 2.4% and 7.4%, respectively, in the evaluation index without reference image quality.Two datasets demonstrate that the proposed restoration can be expected to treat the unnatural restoration, uneven brightness,and reddish color in the traditional underwater image restoration. The finding can also provide a technical reference for underwater image restoration.
作者
郑建华
杨高林
刘双印
曹亮
张子豪
Zheng Jianhua;Yang Gaolin;Liu Shuangyin;Cao Liang;Zhang Zihao(Collage of Information Science and Technology,Zhongkai University of Agriculture and Engineering,Guangzhou 510225,China;Academy of Smart Agricultural Engineering Innovations,Zhongkai University of Agriculture and Engineering,Guangzhou 510225,China;Smart Agriculture Engineering Research Center of Guangdong Higher Education Institutes,Guangzhou 510225,China;Guangdong Provincial Agricultural Products Safety Big Data Engineering Technology Research Center,Guangzhou 510225,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2022年第14期174-182,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金项目(61871475)
广东省重点领域研发计划资助(2020B0202080002,2020B020222003)
广州市创新平台建设计划项目(201905010006)
广州市重点研发计划项目(202103000033)
广东省普通高校创新团队项目(2021KCXTD019)
广东省科技兴农项目(2021KJ383-05,2021KJ383-06)。
关键词
图像处理
渔业
水下图像
图像复原
暗通道先验
颜色校正
背景光
透射图
image processing
fisheries
underwater image
underwater image restoration
dark channel prior
color correction
background light
transmission map
