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孙晓帆,刘浩,张鑫生,吴乐明,况奇刚(东华大学信息科学与技术学院, 上海 201620)

摘 要
目的 在对整个水下图像集的质量进行评价时,现有方法是采用某一质量评价准则的质量分数平均值作为指标,以平均值的高低来说明质量增强算法的优劣,但是,非一致性增强的质量分数平均值会随着图像集的变化而产生较大的波动。为了解决上述问题,本文提出了一个更加具有普适性的水下图像质量评价方法:一致性增强质量评价(CEQA)方法。方法 所提方法通过对比图像增强前后的质量分数差值,来判断增强算法性能的一致性,再通过改变选定的质量分数差值所占权重比例并统一分数制,求出一致性增强的图像集的一致性增强质量评价分数。结果 虽然当图像集较小时,非一致性增强的图像质量增强算法得到的质量分数平均值最高,但当图像集扩大时,其增强后的质量分数平均值却低于原图的质量分数平均值;而在图像集扩展前后,一致性增强的图像质量增强算法能够稳定地增强图像质量,其得到的质量分数平均值一直高于原图的质量分数平均值。结论 本文通过实验证明了所提方法的可行性,扩展应用能够通过本文方法得到有效的实验数据,以对比说明各种水下图像质量增强算法的优劣;本文的方法比平均值方法更加鲁棒有效地控制了大样本偏差。因此,本文为大规模应用中如何选取水下图像集的质量增强算法,提供了一个更好的评价标准。
Consistent enhancement assessment for an underwater image set

Sun Xiaofan,Liu Hao,Zhang Xinsheng,Wu Leming,Kuang Qigang(College of Information Science and Technology, Donghua University, Shanghai 201620, China)

Objective 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 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 ({I1, I2, I3,…,In}, 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 I1 to obtain a quality score α1 of the original image I1. 2) The proposed method can process the original underwater image I1 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 β1. 4) the quality score β1 is subtracted by the quality score α1 to obtain the fractional difference Q1. 5) Steps 1-4 are successively performed for the original underwater images I2, I3,…,In to obtain fractional difference Q2, Q3,…,Qn, correspondingly. If the Q1, Q2,…,Qn 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 Q1, Q2,…,Qn is selected as Qmax and the minimum value as Qmin. The average value of Q1, Q2,…,Qn is determined as Qave. 7) the effective value Ceff 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 Qave and the minimum value Qmin 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 A1, A2,…,Am (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 Qave is different, then the quality enhancement algorithm with high effective value Ceff has significant enhancement strength and improved enhancement capability when comparing several consistent quality enhancement algorithms; if the average value Qave is the same, then the quality enhancement algorithm with high effective value Ceff has an improved stability. Result 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 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.