Objective

2

Due to the complex and changeable underwater environment

when light enters a water body

it is affected by absorption and scattering

which leads to color cast

blur

and low contrast of underwater images. Water has different absorption capabilities for different light rays. As a result

the red channel information of the image is poorly preserved

making underwater imaging difficult. In order to solve the problems caused by light absorption in underwater images

existing scientific research has found that compared with the red and blue channels

the obtained green channel details of the underwater channel are best preserved

whereas the red channel details are poorly preserved. According to the characteristics of underwater imaging

the present study proposes an underwater color image enhancement method based on two-scale image decomposition. The method can effectively solve the problems of color cast and low contrast of the image and reduce the introduction of red shadow to obtain high-resolution underwater images.

Method

2

First

a contrast stretching method based on the gray world hypothesis is used. The method calculates the mean and mean square error of each channel independently and then uses a new normalization method using the mean and mean square error of each channel. In order to reduce the color cast problem of the image

a median filter is used to reduce the noise problem introduced by the red channel contrast stretch. Then

the two-scale image decomposition method is used to apply a large-scale mean filter to the red channel of the median filtered image and the green channel of the image after the contrast is stretched. Subsequently

the channel is divided into a large-scale base layer and a small-scale detail layer. While retaining the large-scale base layer of the red channel of the image

the small-scale detail layer of the green channel is introduced. Because the color and details of the red channel of the original image are equally important

the true details and colors of the red channel of the original image are introduced into the red channel of the processed image to achieve a better restoration of the red channel colors and details of the image. Different underwater images are selected as the experimental dataset

and the proposed method is compared with the dark channel prior method

fusion-based method

automatic red channel recovery method

and deep learning method based on convolutional neural network. First

subjective analysis of the visual effects is conducted

and then objective analysis is performed through different underwater evaluation indicators.

Result

2

The results of different experimental images are compared. The dark channel prior method cannot solve the problem of color cast and low contrast of underwater images. The red channel recovery method solves the problem of image color cast or low contrast

but the image results obtained are poorer than those of the proposed method. The fusion-based method is better than the proposed method in improving image contrast. This is maintained by the edge of the image fusion algorithm

but an obvious red shadow problem will be observed in the resulting image. The proposed method can better solve the red shadow problem in the image. Deep learning method can improve the brightness and clarity of the image

but the result still has a certain color cast problem. Compared with the four methods

the proposed method can better solve the problem of color cast of underwater images

and no red shadow is generated

which is more in line with the visual perception of human eyes. Three methods

namely

natural image quality evaluation method (NIQE)

information entropy (IE)

and underwater image quality measurement method (UIQM) inspired by the human visual system

are used in the experiment for comparative analysis. NIQE compares the test image with the default model calculated from the natural scene image. The lower the index

the better the image perception quality and the higher the clarity. At the same time

NIQE and human eye subjective quality evaluation have better consistency

are similar to the human visual system

and can effectively perform real-time image quality evaluation. IE is an index to measure the information richness of a pair of images. The larger the IE value

the richer the detailed information contained in the image. Finally

UIQM is a better existing underwater image quality evaluation method

which can solve the evaluation of three important indicators of underwater images (i.e.

color

clarity

and contrast). The larger the value of UIQM

the higher the clarity and the better the quality of the image. Compared with fusion-based methods and deep learning methods

NIQE and IE in quantitative analysis are improved by 1.8% and 13.6%

respectively. The value of UIQM is 1.542

which is the best result compared with other methods. The experimental results show that the proposed method can better solve the problem of image color cast and red shadow compared with other methods.

Conclusion

2

First

through the contrast-based stretching method

the green channel image with good detail retention is used to compensate for the red channel image detail. We propose an underwater color image enhancement method based on two-scale image decomposition in this study. The proposed method can better reflect the detail of the red channel and does not lose the color information of the red channel itself. Subjective qualitative analysis and quantitative analysis are used to evaluate the algorithm in this study. According to the experimental results

the proposed method has poor performance on underwater color images with poor red and blue channel information because the method focuses on restoring the details and colors of the red channel. The method is suitable for underwater color images with poor red channel information performance and relatively good green channel and blue channel information performance. Therefore

considering only the red channel may be too restrictive. Next work

we consider the influence of the blue and green channels of the image on the red channel

and improve image clarity through image fusion.