Underwater image quality improvement approach based on an adapted Gabor multi-channels filtering

This paper presents a new approach to improve the identification of underwater fiducial markers for camera pose estimation. The use of marker detection is new in the underwater field. Hence, it requires a new image preprocessing to reach the same performance as in onshore environment. This is a challenging task due to the poor quality of underwater images. Images captured in highly turbid environment are strongly degraded by light attenuation and scattering. In this context, dehazing methods are increasingly used. However, they are less effective because the scattering of light in the water is different from the atmosphere. Therefore, the estimation of dehazing parameters on the target image can lead to a bad image restoration. For this reason, an objectoriented dehazing method is proposed to optimize the contrast of markers. The proposed system exploits the texture features derived by multi-channel filtering for image segmentation. To achieve this, saliency detection is applied to estimate the visually salient objects in the image. The generated saliency map is passed through a Gabor filters bank and the significant texture features are clustered by K-means algorithm to produce the segmented image. Once different objects of the image are separated, an optimized Dark Channel Prior (DCP) dehazing method is applied to optimize the contrast of each individual object. The implemented system has been tested on a large image dataset taken during night offshore experiments in turbid waters at 15 meters depth. Results showed that the object-oriented dehazing improves the successful of markers identification in underwater environment.