面向一致增强评估的子集比例动态选取方法

Subset Ratio Dynamic Selection for Consistency Enhancement Evaluation

水下图像往往质量较低且数量众多,在许多应用中需要对其执行大规模的一致增强。在子集导引一致增强评估准则下,现有的子集选取方法在对原始图像集进行抽样时,所需候选子集的抽样数据过多,且不具备对数据内容的自适应能力。为此,文中将候选子集进一步划分为若干份抽样子集,按照不放回抽样策略进行抽样,并根据一致增强评估准则得到某一待检增强算法对逐份抽样子集的一致性增强度,利用一定置信水平条件下的学生-t分布,自适应地选定子集比例,并预估该增强算法对原始图像集的一致性增强度。实验结果表明,相比现有的子集选取方法,所提方法在各种情况下均能减少原始图像集的抽样数据,同时正确判断出每种增强算法的一致性能。所提方法在保持评估误差相当的条件下,相比子集固定比例方法可减少2%~14%的子集比例,相比逐级递增的方法可减少3%~9%的子集比例,从而鲁棒地降低了子集导引一致增强评估的复杂度。

Due to poor imaging conditions,a lot of underwater images require the consistency enhancement.In the subset-guided consistency enhancement evaluation criterion,the existing subset selection methods need too much subset samples of a whole imageset without any adaptation on data content.Therefore,this paper proposes a subset ratio dynamic selection method for consistency enhancement evaluation.The proposed method further divides the candidate samples into several sampling subsets.Based on a non-replacement sampling strategy,the consistency enhancement degree of an enhancement algorithm is obtained for each sampling subset.By using the student-t distribution under a certain confidence level,the proposed method can adaptively determine the subset ratio for a whole imageset,and the candidate subset is used to predict the consistency enhancement degree of the enhancement algorithm on the whole imageset.Experimental results show that as compared with the existing subset selection methods,the proposed method can reduce the subset ratio in all cases,and correctly judge the consistency performance of each enhancement algorithm.With similar evaluation error,the subset ratio of the proposed method can be decreased by 2%~14% over that of the fixed ratio method,and be decreased by 3%~9% over that of the gradual addition method,and thus the complexity is robustly reduced during subset-guided consistency enhancement evaluation.