Objective

2

Numerous restoration algorithms for single images exist. They are remarkable in the defogging of sky images

but most of them cannot be applied directly to the restoration of underwater images. Image restoration aims to process degenerated images to recover the original image (before degeneration). Underwater illumination is insufficient and unevenly distributed

and these light variations affect the results obtained by restoration methods of single images. In general

the results are unsatisfactory. Polarization is the basic feature of light

and the reflected light of underwater objects are mostly partially polarized. Therefore

polarized underwater images have special polarization characteristics

and underwater image restoration based on multiple polarized images has gradually become popular in recent years. Focusing on the mistiness and unobvious details of underwater polarized images

a restoration method for underwater polarized images based on structural similarity is proposed. This method is expected to improve the clarity

contrast

and color fidelity of images.

Method

2

First

images taken through a polarizer at orthogonal orientations are obtained. The images have the best and worst backscatters. The water transmittance is only related to the depth of field and the attenuation coefficient of the water body; the object radiance depends on the incident light and the surface characteristics of the object. Therefore

we can assume that they are mutually independent. The structural similarity can measure the similarity of two images from brightness

contrast

and structure and can directly describe the correlation between the two images. Second

on the basis of the irrelevance relationship between the transmittance and the object radiance

the solution formula of water transmittance is derived by the structural similarity. The difference of the two polarized images is also the difference of the background lights in these images. This difference is also the function of depth of field. Thus

the polarized-difference image is used for calculating the initial value of transmittance during the iterative solution. An accurate transmittance is necessary for the good restoration of images. Finally

object radiance is obtained by inversing the underwater polarization imaging model

and color is corrected to produce the restored image. The color correction is based on a single point and chooses the point that has well-kept color information as the reference pixel. Then

the global pixels are normalized by the reference pixel to realize the color correction of the entire image.

Result

2

In the experiment

the proposed algorithm is compared with two other polarized restoration algorithms to test its effectiveness

and several groups of underwater polarized images are selected as research objects. The images used in this study were obtained from relevant studies. Quantitative indicators

such as contrast

information entropy

gray mean grads (GMG)

peak signal-to-noise ratio (PSNR)

measure of enhancement (EME)

and runtime

are used for evaluating the effect. Results show that the contrast

information entropy

and GMG of our method are better than those of the two other algorithms. Moreover

a great restoration improvement effect is achieved. The YY algorithm removes the blur of the original images to a certain extent

but certain object areas of the recovered images are supersaturated. The images restored by the Huang algorithm are generally too dark to enable the identification of the scene details due to the inaccurate estimation of the degree of polarization of the object radiance. A comparison of the evaluation parameters shows that the contrast and GMG of our method are twice as high as those of the YY algorithm. Furthermore

the color distribution of the images recovered by our method are more homogeneous than those by YY

thus resulting in sufficient image information and the highest information entropy. The prominent EME also shows that our result has clear texture

high contrast

and good restoration. Certain color channels of the images obtained by the Huang algorithm are not recovered; thus

they have single color tones and the values of several color channels are as low as those of the raw images

thereby resulting in a small mean square error and an extremely high PSNR. In terms of time cost

our method and the Huang algorithm run relatively longer than YY because of the traversal process of the parameters.

Conclusion

2

On the basis of the analysis of the underwater polarization imaging model and the statistical independence relationship between the object radiance and water transmittance

image restoration is conducted successfully after the estimation of transmittance. The problems of blurred details and low contrast in polarized underwater images are effectively solved. The results of the subjective and objective analyses show that the proposed algorithm can recover polarized underwater images effectively and obtain restored images with high contrast

obvious details

and rich color. Compared to other algorithms

the proposed algorithm can improve the contrast

clarity

and color balance of polarized underwater images significantly

thus providing an important foundation of underwater target recognition and analysis.