Abstract
Underwater video images, as the primary carriers of underwater information, play a vital role in human exploration and development of the ocean. Due to the optical characteristics of water bodies, underwater video images generally have problems such as color bias and unclear image quality, and image quality degradation is severe. Degenerated images have adverse effects on the visual tasks of underwater vehicles, such as recognition and detection. Therefore, it is vital to obtain high-quality underwater video images. Firstly, this paper analyzes the imaging principle of underwater images and the reasons for their decline in quality and briefly classifies various existing methods. Secondly, it focuses on the current popular deep learning technology in underwater image enhancement, and the underwater video enhancement technologies are also mentioned. It also introduces some standard underwater data sets, common video image evaluation indexes and underwater image specific indexes. Finally, this paper discusses possible future developments in this area.
Highlights
The ocean covers 71% of the Earth’s surface, with a total area of 360 million square kilometers, and contains rich resources
Median filtering is used to estimate depth of field, and a color correction method is introduced Combined wavelength compensation and image dehazing (WCID) Underwater dark channel prior (UDCP) method considering only blue and green channels DCP algorithm improved by using minimization of reverse red channel and blue-green channel Red channel uses gray world color correction algorithm, and blue and green channels use the DCP algorithm Different strategies selected to restore RGB combined with maximum posterior probability (MAP) sharpening
Extracts features from low-resolution images after subsampling; subsampling and IIR gaussian filter are used to form a fast filter to complete the fast two-bit convolution operation The convolutional layer in the network structure of UMCNN is not connected to other convolutional layers in the same block and the network does not use any full connection layer or batch normalization processing In the generator part, the model only learns 256 feature graphs of size 8 × 8; in the discriminant part, the recognition is only based on patch-level information The time-bilateral filtering strategy is used for the white balance version of the video frame
Summary
The ocean covers 71% of the Earth’s surface, with a total area of 360 million square kilometers, and contains rich resources. Guided filtering was used to improve the algorithm, reducing the time required for underwater image processing They proposed a method for adaptive feedback stretching of saturation that can maintain the structural information of the underwater image while improving the clarity of the image, but the global contrast enhancement is not significant enough. A maximum a posteriori formulation for underwater image enhancement is established on the color-corrected image by imposing multiorder gradient priors on reflectance and illumination This algorithm has the effectiveness of the proposed method in color correction, naturalness preservation, structures and details promotion, artifacts or noise suppression. The results show that this method can be applied to water degradation images in different environments effectively solving color distortion, low contrast, and unobvious details of underwater images. Eliminates color deviation, achieves high-quality fusion and a better de-hazing effect
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