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发布时间: 2018-11-16
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DOI: 10.11834/jig.180255
2018 | Volume 23 | Number 11




    NCIG 2018会议专栏    




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面向水下图像集的一致性增强评价方法
expand article info 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。

关键词

图像集; 一致性增强; 图像质量; 图像增强; 质量评价

Consistent enhancement assessment for an underwater image set
expand article info Sun Xiaofan, Liu Hao, Zhang Xinsheng, Wu Leming, Kuang Qigang
College of Information Science and Technology, Donghua University, Shanghai 201620, China
Supported by: Shanghai Municipal Natural Science Foundation (18ZR1400300)

Abstract

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 ({ $ {{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 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.

Key words

image set; consistent enhancement; image quality; image enhancement; quality assessment

0 引言

近年来不断有研究人员提出新的水下图像增强和复原算法,而水下图像集的增强性能主要通过图像质量评价准则来衡量,在通过质量评价准则得到各幅图像的质量分数后,通常使用该质量分数的平均值来论述质量增强算法的优劣。然而,大量的实验发现,质量增强算法改进后的图像质量评价分数不能一致性地高于原图像的质量评价分数,也就是说,这样的图像质量增强算法只能增强部分图像的性能,无法对所有图像进行增强。

随着大数据应用的不断发展,需要处理的图像集不断扩大,图像质量增强算法对全样本进行增强,这样会导致抽样增强所得的结果误差在全样本上被放大,因此,使用质量分数的平均值来说明质量增强算法的优劣,实用性较差。为解决这一问题,本文提出一种一致性增强的图像集质量评价方法,为水下图像质量增强算法在实际大规模图像集中的应用,提供了一个更好的评价标准。

1 评价准则与增强算法

1.1 单幅图像的质量评价准则

Yang和Arcot提出了质量评价准则UCIQE(underwater colour image quality evaluation)[1],该准则是目前应用最广泛的水下图像质量评价准则,UCIQE通过线性组合饱和度、色度与对比度来对水下图像的模糊、非均匀色差和对比度进行量化评估。香农(Shannon)[2]提出信息熵将热力学概率扩展到系统中各个信息源信号出现的概率。通常情况下,信息熵越大,说明该质量增强算法更加有效地减少了图像信息丢失,并更好地增加了有价值的信息[3]

1.2 典型的图像质量增强算法

本文分析了应用较为广泛的经典水下图像质量增强算法:逆滤波算法[4]、均值滤波算法[5]、中值滤波算法[6]、导向滤波暗通道去雾算法[7]、可见度复原算法[8]、动态阈值白平衡算法[9]、直方图均衡(HE)算法[10]、对比度受限的自适应直方图均衡化(CLAHE)算法[11]、CLAHE-RGB算法[11]、CLAHE-HSV算法[11]、对比度拉伸算法[12]与无监督颜色模型算法[13]等。

2 图像集的平均值方法

2.1 平均值方法的定性分析

将不同的水下图像增强算法对图 1(a)进行图像增强,得到不同的增强效果,如图 1(b)(m)所示。

图 1 不同增强算法的质量定性比较
Fig. 1 Qualitative comparison of different enhancement algorithms((a) original underwater image; (b) inverse filtering; (c) mean filtering; (d) median filtering; (e) guided filtering dark channel fogging; (f) visibility restoration; (g) dynamic threshold white balance; (h) histogram equalization; (i) CLAHE; (j) CLAHE-RGB; (k); CLAHE-HSV; (l) contrast extension; (m) unsupervised color model)

针对图 1中典型算法的处理结果,采用UCIQE和熵两种客观的图像质量评价准则进行评测,图 2给出图 1结果的质量评价分数曲线。

图 2 各增强算法增强后的图像与原图的质量分数值比较
Fig. 2 Comparison of quality scores between original image and enhanced images by different enhancement algorithms
((a) UCIQE value; (b) entropy value)

图像质量评价准则UCIQE和熵的评价标准均为:分数值越高表明图像质量越好。因此,根据图 2可以看出,大部分的经典增强算法对图像增强后所得到的质量分数会低于原图的质量分数,也就是说,目前大部分的经典增强算法,都存在着降低原图像的质量评分的情况。

2.2 平均值方法的定量分析

为了避免定性实验存在主观性,我们采用定量实验对图 3所示的12幅典型图像组成的图像集进行实验。

图 3 典型水下图像
Fig. 3 Typical underwater images ((a) Ancuti1; (b) Ancuti2; (c) Ancuti3; (d) Cc_land; (e) Cc_sea; (f) Eustice; (g) fish; (h) Galdran; (i) ocean; (j) reef1; (k) reef2; (l) reef3)

在使用第2.2节所述的图像增强算法对图像进行增强并得出质量评价分数后,将每种算法对各个图像增强前后的UCIQE值与熵值分别进行比较,并求出平均值,取3种典型算法所得的结果展示如下:表 1为这3种质量增强算法对上述图像集增强前后的UCIQE值与熵值的平均分对比。

表 1 UCIQE值与熵值的平均分对比
Table 1 The comparison of average value of UCIQE and entropy

下载CSV
增强前后 原图 直方图均衡 CLAHE 无监督颜色模型
UCIQE 0.52 0.62 0.61 0.59
7.35 7.43 7.62 7.49

表 1可以看出,这3种图像质量增强算法对图像进行增强后,所得到的UCIQE值与熵值的平均分都大于原图的UCIQE值与熵值的平均分。而在图 4中可以看到,这3种图像质量增强算法存在大量的降低图像质量分数的情况。那么如果当图像集扩大时,就更加无法保证对图像的一致性增强,会出现更多的被降低图像质量的情况,所以我们提出如下观点:图像质量评价分数的平均值更高,并不能说明该图像质量增强算法更好,因为不能保证该算法能够一致性地增强图像,如果对大量图像集进行应用,会产生严重的偏差。

图 4 图像增强前后的UCIQE值与熵值的具体质量分数对比
Fig. 4 Comparison of the specific quality scores of UCIQE value and entropy value before and after image enhancement
((a) histogram equalization; (b) CLAHE; (c) unsupervised color model)

3 本文方法

为了解决上述问题,本文提出一种全新的鲁棒性更高的图像集增强评价方法:一致性增强质量评价(CEQA)方法。本文面向水下图像集所提出的CEQA方法的质量评价体系如图 5所示,其流程图如图 6所示。

图 5 CEQA方法的质量评价体系
Fig. 5 Quality evaluation system of CEQA method
图 6 CEQA方法的流程图
Fig. 6 Flow chart of the CEQA method

CEQA方法的具体步骤为:

1) 首先确定某一图像集{$ {{\mathit{\boldsymbol{I}}}_{1}}, {{\mathit{\boldsymbol{I}}}_{2}}, {{\mathit{\boldsymbol{I}}}_{3}}, \cdots , {{\mathit{\boldsymbol{I}}}_{n}}$}($ n$为该水下图像集的图像总数量), 再选取一种质量评价准则$ M$对水下获取的原始图像$ {{\mathit{\boldsymbol{I}}}_{1}}$的图像质量进行评价,得出一个原图像$ {{\mathit{\boldsymbol{I}}}_{1}}$的质量分数$ {{\alpha }_{1}}$,作为公式中的参数之一,也作为评价图像质量增强算法好坏的基准。其中,图像集{$ {{\mathit{\boldsymbol{I}}}_{1}}, {{\mathit{\boldsymbol{I}}}_{2}}, {{\mathit{\boldsymbol{I}}}_{3}}, \cdots , {{\mathit{\boldsymbol{I}}}_{n}}$}与质量评价准则$ M$为实验变量。

2) 接下来,将这幅水下获取的原始图像$ {{\mathit{\boldsymbol{I}}}_{1}}$通过图像质量增强算法$ A$处理,得到增强后的图像$ {{{\mathit{\boldsymbol{{I}'}}}}_{1}}$,其中,质量增强算法$ A$为实验对象,是该次验证评价的主要算法。

3) 使用步骤1)中使用的质量评价准则$ M$对增强后的图像$ {{{\mathit{\boldsymbol{{I}'}}}}_{1}}$做质量评价,得到质量分数$ {{\beta }_{1}}$,若增强后的图像质量分数$ {{\beta }_{1}}$高于基准值$ {{\alpha }_{1}}$,则$ {{Q}_{1}}$值为正,可以说明在质量评价准则$ M$的检测下,图像质量增强算法$ A$可以对这一幅原始图像$ {{\mathit{\boldsymbol{I}}}_{1}}$增强。

4) 将得到的质量分数$ {{\alpha }_{1}}$$ {{\beta }_{1}}$代入求$ {{Q}_{i}}$

$ {{Q}_{i}}={{\beta }_{\mathit{i}}}-{{\alpha }_{\mathit{i}}} $ (1)

式中,$ i$($ i$=1,2,…,$ n$)为图像标号,为使计算简便,使$ i$与重复次数保持一致后得到$ {{Q}_{i}}$值。

5) 依次对水下获取的原始图像$ {{\mathit{\boldsymbol{I}}}_{2}}, {{\mathit{\boldsymbol{I}}}_{3}}, \cdots , {{\mathit{\boldsymbol{I}}}_{n}}$重复步骤1)—4),分别得到$ {{Q}_{2}}, {{Q}_{3}}, \cdots , {{Q}_{n}}$分数。若$ {{Q}_{i}}$($ i$=1,2,…,$ n$)值全为正,则说明,这一水下图像质量增强算法$ A$在质量评价准则$ M$的评价条件下,能够对该水下图像集进行一致性增强,则继续进行步骤6),否则,得出结论:该质量增强算法$ A$为非一致性增强的。

6) 首先求出$ {{Q}_{i}}$的最大值

$ {{Q}_{{\rm max}}}={\rm max}\left\{ {{Q}_{1}}, \cdots , {{Q}_{\mathit{i}}}, \cdots , {{Q}_{n}} \right\} $ (2)

求出$ {\rm CEQ}{{{\rm A}}_{\mathit{i}}}$的最小值

$ {{Q}_{{\rm min}}}={\rm min}\left\{ {{Q}_{1}}, \cdots , {{Q}_{\mathit{i}}}, \cdots , {{Q}_{n}} \right\} $ (3)

并求出$ {\rm CEQ}{{{\rm A}}_{\mathit{i}}}$的平均值

$ {{Q}_{{\rm ave}}}={\rm ave}\left\{ {{Q}_{1}}, \cdots , {{Q}_{\mathit{i}}}, \cdots , {{Q}_{n}} \right\} $ (4)

7) 得到在质量评价准则$ M$下,这一水下图像质量增强算法$ A$对该图像集的$ {C_{{\rm{eff}}}}$值,定义为

$ \begin{array}{l} {C_{{\rm{eff}}}} = \lambda \frac{{{Q_{{\rm{ave}}}}}}{{{Q_{{\rm{max}}}} - {Q_{{\rm{min}}}}}} + \\ \;\;\left( {1{\rm{ - }}\lambda } \right)\frac{{{Q_{{\rm{min}}}}}}{{{Q_{{\rm{max}}}} - {Q_{{\rm{min}}}}}} \end{array} $ (5)

式中,$ \lambda $为权重系数。

对于同一水下图像集,在选定的质量评价准则$ M$的评价条件下,对不同的水下图像质量增强算法$ {A_1}, {A_2}, \cdots , {A_m}$($ m$为所需对比的质量增强算法的总数量)进行评估,所得到的非一致性增强的质量增强算法,无法在质量评价准则$ M$下对该图像集进行一致性增强;反之,所得到的一致性增强的质量增强算法,能够在质量评价准则$ M$下对该图像集进行一致性增强。此外,一致性增强的多个质量增强算法进行比较时,若$ {{Q_{{\rm{ave}}}}}$值不同,则$ {C_{{\rm{eff}}}}$值越高的增强算法,其增强强度越大,增强能力越好;若$ {{Q_{{\rm{ave}}}}}$值相同,则$ {C_{{\rm{eff}}}}$值越高的增强算法,其稳定性越好。

4 评价准则与增强算法

4.1 CEQA方法的扩展应用

图 3所示的12幅图像进行CEQA方法的扩展应用,得到该图像集在质量评价准则UCIQE下,只有逆滤波、导向滤波暗通道去雾和CLAHE-HSV算法为一致性增强的质量增强算法。接下来,取$ \lambda = \frac{1}{2}$,分别对逆滤波、导向滤波暗通道去雾和CLAHE-HSV算法,进行计算与比较,得到各$ {C_{{\rm{eff}}}}$值如表 2所示。

表 2 一致性增强的$ {C_{{\rm{eff}}}}$
Table 2 The consistent enhancement $ {C_{{\rm{eff}}}}$ value

下载CSV
图像质量增强算法 逆滤波 导向滤波暗通道去雾 CLAHE-HSV
$ {C_{{\rm{eff}}}}$ 1.66 1.62 2.82

表 2可以看出,该水下图像集在UCIQE评价准则下,CLAHE-HSV的$ {C_{{\rm{eff}}}}$质量分数最高,也就是说CLAHE-HSV的增强效果最好,逆滤波算法优于导向滤波暗通道去雾算法,有一定的增强能力,但仍有较大的可优化空间。

4.2 CEQA方法与平均值方法的对比实验

为了说明CEQA方法可以比平均值方法更好地评价图像质量增强算法的优劣[14-16],选取100幅水下图像作为水下图像集$ \mathit{\boldsymbol{b}}$,并随机选取两种图像质量增强算法动态阈值白平衡算法与对比度拉伸算法,分别增强该图像集的图像质量,得到增强后的图像,再使用质量评价准则UCIQE对以上增强前后的图像进行图像质量评价,取$ \lambda = \frac{1}{2}$,且随机选取其中的5幅图像作为图像集$ \mathit{\boldsymbol{a}}$:{图像1~图像5},对比全部100幅图像的水下图像集$ \mathit{\boldsymbol{b}}$,得到图像集$ \mathit{\boldsymbol{a}}$中各图像的质量分数如图 7所示,对比结果如表 3所示。

图 7 图像集$ \mathit{\boldsymbol{a}}$中各图像的图像质量分数
Fig. 7 Image quality fraction of each image in the image set $ \mathit{\boldsymbol{a}}$

表 3 $ {C_{{\rm{eff}}}}$与平均值对比(在UCIQE准则下)
Table 3 The $ {C_{{\rm{eff}}}}$ value compared with average value (by using the UCIQE criterion)

下载CSV
方法 图像集 原图 动态阈值白平衡 对比度拉伸
平均值 图像集$ \mathit{\boldsymbol{a}}$ 0.420 0.426 0.425
图像集$ \mathit{\boldsymbol{b}}$ 0.427 0.407 0.434
$ {C_{{\rm{eff}}}}$ 图像集$ \mathit{\boldsymbol{a}}$ 非一致性增强 19.168
图像集$ \mathit{\boldsymbol{b}}$ 7.652

图 7可以看出,当图像集为5幅图像时,经过动态阈值白平衡增强后的图像存在降低图像质量的情况,属于非一致性增强,而对比度拉伸算法是一致性增强的图像质量增强算法。由表 3可知,当图像集较小时,虽然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,但当图像集由5幅扩大到100幅时,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,也就是说,非一致性增强的图像质量增强算法整体上降低了图像质量;而此时对比度拉伸为一致性增强的图像质量增强算法,达到了整体质量上对图像的一致性增强。可见,当非一致性增强的图像质量增强算法应用的图像集变化时,其增强后的图像质量分数变化较大,会随着图像集的扩大而产生较大的偏差,只有当该图像质量增强算法为一致性增强图像质量算法时,才能说明该算法的扩展性更强、实用性更好。

5 结论

本文提出的CEQA方法,结合现有的经典质量评价准则,可以为图像集的质量增强算法提供一个评价标准,而CEQA方法比现有的平均值方法对于图像集的评价性能更优,对新的图像质量增强算法的提出给出了可量化的性能指标。CEQA方法对于今后提出的水下图像质量增强算法的优劣,具有指导作用。此外,本文方法的公式简单易懂、迁移度高、普适性好,可适用于各种图像质量评价领域。CEQA方法仍然存在的缺陷是:对增强算法的增强稳定性要求很严格,更适合在零容错的应用领域,为面临未知场景的应用领域选取可靠的质量增强算法,而对于普通的质量增强应用需求,由于本方法的要求较高,一部分增强稳定性波动较大的方法无法通过本方法的评估。

水下图像增强技术仍然具有较大的发展空间,水下图像集的增强性能需要更权威的评价方法。本文进一步的研究方向将侧重于:如何使所提方法的一致性更为可调,在面向具体应用时,可以针对不同的应用需求,选取不同的质量增强算法。不仅可以在严格条件下筛选质量增强算法,也可以根据指定的需求,改变筛选条件,得到特定容错度的实验数据与结果。

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