Objective

2

An increasing number of underwater image enhancement methods have been put into practical applications because underwater images typically have quality degradation problems

such as blurring

distortion

and low visibility. At present

each quality evaluation criterion mainly focuses on the single image. Existing methods adopt the average quality score of a quality evaluation criterion as an indicator

and the enhancement algorithm is evaluated by the average score. However

the non-consistent average quality score changes with the image set and produces large fluctuations. If an enhancement algorithm cannot consistently improve the image quality score in a small-scale image set

then the average quality score has certain limitations and large error when the enhancement algorithm is applied to a large-scale image set. To solve the abovementioned problems

a universal underwater image quality assessment method

namely

consistent enhancement quality assessment (CEQA) for an underwater image set

is proposed.

Method

2

The proposed method can judge the consistency of the enhancement algorithm by comparing the difference of the quality score before and after image enhancement and by changing the weight proportion of the selected quality score difference

unifying the fractional system

and calculating the CEQA fraction of the enhanced image set. The concrete steps of this proposed method are as follows:1) An image set ({

$$ {{I}_{1}}

\; {{I}_{2}}

\;{{I}_{3}}

\;\cdots

\;{{I}_{n}}$$

}

where

$$ n$$

is the total number of images of the underwater image set) is determined

and then a quality evaluation criterion M is selected to evaluate the image quality of the original underwater image

$$ I_1$$

to obtain a quality score

$$ {{\alpha }_{1}}$$

of the original image

$$ I_1$$

. 2) The proposed method can process the original underwater image

$$ I_1$$

through the image quality enhancement algorithm A

and obtain the enhanced image

$$ {{{{I}'}}_{1}}$$

. 3) the proposed method uses the quality evaluation criterion M

which is used in Step 1

to evaluate the quality of the enhanced image

$$ {{{{I}'}}_{1}}$$

and obtain the quality score

$$ {{\beta }_{1}}$$

. 4) the quality score

$$ {{\beta }_{1}}$$

is subtracted by the quality score

$$ {{\alpha }_{1}}$$

to obtain the fractional difference

$$ Q_1$$

. 5) Steps 1-4 are successively performed for the original underwater images

$$ I_2$$

$$ I_3$$

…

$$ I_n$$

to obtain fractional difference

$$ {{Q}_{2}}

\; {{Q}_{3}}

\; \cdots

\; {{Q}_{n}}$$

correspondingly. If the

$$ {{Q}_{1}}

\; {{Q}_{2}}

\; \cdots

\;{{Q}_{n}}$$

values are all positive

then the underwater image quality enhancement algorithm A

under the quality evaluation criterion M

can consistently enhance the quality of this underwater image set

and then Step 6 is performed. Otherwise

the quality enhancement algorithm A is an inconsistent quality enhancement algorithm under these conditions. 6) the maximum value of

$$ {{Q}_{1}}

\;{{Q}_{2}}

\;\cdots

\;{{Q}_{n}}$$

is selected as

$$ Q_{\rm max}$$

and the minimum value as

$$ Q_{\rm min}$$

. The average value of

$$ {{Q}_{1}}

\; {{Q}_{2}}

\;\cdots

\;{{Q}_{n}}$$

is determined as

$$ Q_{\rm ave}$$

. 7) the effective value

$$ C_{\rm eff}$$

of the underwater image quality enhancement algorithm A for this image set is obtained under the quality evaluation criterion M by normalizing the average value

$$ Q_{\rm ave}$$

and the minimum value

$$ Q_{\rm min}$$

and then adjusting its proportion. Under the selected quality evaluation criterion M

the non-consistent quality enhancement algorithm for the same underwater image set cannot consistently enhance the image set under the quality evaluation criterion M to evaluate different underwater image quality enhancement algorithms

$$ {{{\rm A}}_{1}}

\; {{{\rm A}}_{2}}

\; \cdots

\; {{{\rm A}}_{m}}$$

(

$$ m$$

is the total number of the quality enhancement algorithms). By contrast

the consistent quality enhancement algorithm can effectively enhance the quality of the image set. If the average value

$$ Q_{\rm ave}$$

is different

then the quality enhancement algorithm with high effective value

$$ C_{\rm eff}$$

has significant enhancement strength and improved enhancement capability when comparing several consistent quality enhancement algorithms; if the average value

$$ Q_{\rm ave}$$

is the same

then the quality enhancement algorithm with high effective value

$$ C_{\rm eff}$$

has an improved stability.

Result

2

The experimental results of the quantitative analysis of the mean value method show that the average value is larger in UCIQE and entropy than in the original image after the image set is enhanced by the three image quality enhancement algorithms

which are randomly selected. However

the quality score is lower in numerous single images than in the original image. In the extended application of CEQA method

using the UCIQE evaluation criteria of a selected underwater image set

the enhancement effect of the CLAHE-HSV algorithm is optimal

and the inverse filtering algorithm is better than the filtering-guided dark channel defogging algorithm. Many experimental data show that our method can effectively solve these problems and provide an evaluation criterion for the quality enhancement algorithm of the image set. The comparative experimental results between the CEQA and the mean value methods show that the non-consistent quality enhancement algorithm has the highest mean value when the image set is small

but its mean value is lower than that of the original image when the image set is enlarged. Therefore

the inconsistent quality enhancement algorithm has an extensive or serious reduction of image quality. The consistent quality enhancement algorithm before and after expanding the image set can steadily improve the image quality

and thus the average value of the quality score is consistently higher than the mean value of the original image.

Conclusion

2

The experimental results of the extended application of the CEQA method show that the proposed method is feasible and can obtain effective experimental data to compare the advantages and disadvantages of underwater image quality enhancement algorithms. The experimental results of the comparison between the CEQA and average value methods suggest that the proposed method is more accurate than the average value method and effectively controls the large sample deviation. Therefore

a consistent enhancement assessment method for the underwater image quality is proposed; this method provides an improved evaluation criterion for underwater image quality enhancement algorithm in large-scale practical applications. The proposed consistent enhancement evaluation method is better than the existing mean value method for evaluating an image set and provides a quantifiable performance index for the new image quality enhancement algorithm. The proposed method has a guiding effect on the advantages and disadvantages of a new image quality enhancement algorithm in the future. In addition

the formula of this method is simple

universal

highly flexible

and easy to understand; this formula can be applied to various fields of image quality evaluation. The shortcoming of the proposed method includes a high requirement for robustness and stability of an enhancement algorithm. The formula is suitable for the applications in the zero-fault-tolerance field. A reliable quality enhancement algorithm is selected for applications with stringent performance requirements. For common application requirements

the standard performance of this method is relatively high

and several algorithms without fluctuation cannot satisfy the requirements of this consistent enhancement assessment method. The underwater image enhancement technology can still be developed

and the enhancement performance requires added authoritative evaluation criterion. Future research should focus on developing an additional fault-tolerant method

and a favorable quality enhancement algorithm can be selected for different application requirements when facing a certain application. An application can select a quality enhancement algorithm under strict conditions

can change the screening conditions in accordance with the specified requirements

and obtain the specific experimental data and results.