计算机科学 ›› 2021, Vol. 48 ›› Issue (2): 153-159.doi: 10.11896/jsjkx.200800188
王凯巡1, 刘浩1,2, 沈港1, 时庭庭1
WANG Kai-xun1, LIU Hao1,2, SHEN Gang1, SHI Ting-ting1
摘要: 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。
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