自动化学报Issue(2)：270-278,9.DOI:10.16383/j.aas.2016.c140854
基于分类学习的去雾后图像质量评价算法
A Quality Assessment Method with Classified-learning for Dehazed Images
摘要
Abstract
Since existing quality assessment methods suffer from poor pertinence and low efficiency, a novel quality assessment method based on classified learning for dehazed images is proposed. In this paper, firstly the metrics interms of image enhancement, image restoration, statistical prior, and human visual system are extracted by analyzing qualitative characteristics of images after haze removal. Then the quality assessment problem is converted to the classification problem by means of support vector machine using our database. Experimental results demonstrate that compared with other state-of-the-art methods the proposed method is highly efficient and practical with subjective and objective consistency.关键词
图像去雾/图像质量评价/支持向量机/暗通道先验/人类视觉系统Key words
Image dehazing/image quality assessment/support vector machine (SVM)/dark channel prior (DCP)/human visual system (HVS)引用本文复制引用
南栋,毕笃彦,马时平,凡遵林,何林远..基于分类学习的去雾后图像质量评价算法[J].自动化学报,2016,(2):270-278,9.基金项目
国家自然科学基金(61372167,61379104)资助@@@@Supported by National Natural Science Foundation of China (61372167,61379104) （61372167,61379104）
