Image Color Correction Based on Double Transmission Underwater Imaging Model

2019 ◽  
Vol 39 (9) ◽  
pp. 0901002
Author(s):  
王国霖 Guolin Wang ◽  
田建东 Jiandong Tian ◽  
李鹏越 Pengyue Li
2019 ◽  
Vol 16 (5) ◽  
pp. 172988141986446
Author(s):  
Xiaojun Wu ◽  
XingCan Tang

Light changes its direction of propagation before entering a camera enclosed in a waterproof housing owing to refraction, which means that perspective imaging models in the air cannot be directly used underwater. In this article, we propose an accurate binocular stereo measurement system in an underwater environment. First, based on the physical underwater imaging model without approximation and Tsai’s calibration method, the proposed system is calibrated to acquire the extrinsic parameters, as the internal parameters can be pre-calibrated in air. Then, based on the calibrated camera parameters, an image correction method is proposed to convert the underwater images to air images. Thus, the epipolar constraint can be used to search the matching point directly. The experimental results show that the proposed method in this article can effectively eliminate the effect of refraction in the binocular vision and the measurement accuracy can be compared with the measurement result in air.


Author(s):  
Yang Wang ◽  
Yang Cao ◽  
Jing Zhang ◽  
Feng Wu ◽  
Zheng-Jun Zha

Underwater imaging often suffers from color cast and contrast degradation due to range-dependent medium absorption and light scattering. Introducing image statistics as prior has been proved to be an effective solution for underwater image enhancement. However, relative to the modal divergence of light propagation and underwater scenery, the existing methods are limited in representing the inherent statistics of underwater images resulting in color artifacts and haze residuals. To address this problem, this article proposes a convolutional neural network (CNN)-based framework to learn hierarchical statistical features related to color cast and contrast degradation and to leverage them for underwater image enhancement. Specifically, a pixel disruption strategy is first proposed to suppress intrinsic colors’ influence and facilitate modeling a unified statistical representation of underwater image. Then, considering the local variation of depth of field, two parallel sub-networks: Color Correction Network (CC-Net) and Contrast Enhancement Network (CE-Net) are presented. The CC-Net and CE-Net can generate pixel-wise color cast and transmission map and achieve spatial-varied color correction and contrast enhancement. Moreover, to address the issue of insufficient training data, an imaging model-based synthesis method that incorporates pixel disruption strategy is presented to generate underwater patches with global degradation consistency. Quantitative and subjective evaluations demonstrate that our proposed method achieves state-of-the-art performance.


2009 ◽  
Vol 34 (17) ◽  
pp. 2688 ◽  
Author(s):  
Weilin (Will) Hou

Author(s):  
L. Shen ◽  
Y. Zhao

Abstract. The need of high-quality underwater imaging is obviously required in many underwater applications. For example, underwater archaeology, underwater ecological research, underwater object detection and tracking. This paper presents a joint enhancing and denoising scheme for an image taken in underwater conditions. Conventional image enhancing methods may amplify the noise depending on the distance and density of the particles in the water. To suppress the noise and improve the enhancement performance, an imaging model is modified by adding the process of amplifying the noise in underwater conditions. This model offers depth-chromaticity compensation regularization for the transmission map and chromaticity-depth compensation regularization for enhancing the image. The proposed iterative underwater image enhancing method with polarization uses these two joint regularization schemes and the relationship between the transmission map and enhanced irradiance image. The transmission map and irradiance image are used to promote each other. To verify the effectiveness of the algorithm, polarizing images of different scenes in different conditions are collected. Different algorithms are applied to the original images. Experimental results demonstrate that the proposed scheme increases visibility in extreme conditions without amplifying the noise.


Author(s):  
G. Bianco ◽  
M. Muzzupappa ◽  
F. Bruno ◽  
R. Garcia ◽  
L. Neumann

Recovering correct or at least realistic colors of underwater scenes is a very challenging issue for imaging techniques, since illumination conditions in a refractive and turbid medium as the sea are seriously altered. The need to correct colors of underwater images or videos is an important task required in all image-based applications like 3D imaging, navigation, documentation, etc. Many imaging enhancement methods have been proposed in literature for these purposes. The advantage of these methods is that they do not require the knowledge of the medium physical parameters while some image adjustments can be performed manually (as histogram stretching) or automatically by algorithms based on some criteria as suggested from computational color constancy methods. One of the most popular criterion is based on gray-world hypothesis, which assumes that the average of the captured image should be gray. An interesting application of this assumption is performed in the Ruderman opponent color space lαβ, used in a previous work for hue correction of images captured under colored light sources, which allows to separate the luminance component of the scene from its chromatic components. In this work, we present the first proposal for color correction of underwater images by using lαβ color space. In particular, the chromatic components are changed moving their distributions around the white point (white balancing) and histogram cutoff and stretching of the luminance component is performed to improve image contrast. The experimental results demonstrate the effectiveness of this method under gray-world assumption and supposing uniform illumination of the scene. Moreover, due to its low computational cost it is suitable for real-time implementation.


2021 ◽  
Vol 336 ◽  
pp. 06033
Author(s):  
Zhengping Sun ◽  
Fubing Li ◽  
Yuying Yang

The main reason for the degradation of the underwater image is the light absorption and scattering. The images are captured in the underwater environment often have some problems such as loss of image information, low contrast, and color distortion. In order to solve the above problems, this paper proposes an image enhancement method for the underwater environment. With the help of the underwater imaging model and dark channel prior theory, a new idea of adding transmission correction and color compensation to G and B color channels is proposed. Experimental results show that, compared with the traditional methods, this method has a better effect on the underwater image with less color deviation.


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