Discriminative feature representation for Noisy image quality assessment
Discriminative feature representation for Noisy image quality assessment
复制标题
用于噪声图像质量评估的判别特征表示
DOI：
10.1007/s11042-019-08424-0
复制
发表时间：
2020-01-02
影响因子：
3.6
通讯作者：
Coatrieux, Gouenou
中科院分区：
文献类型：
--
作者：
Gu, Yunbo;Tang, Hui;Coatrieux, Gouenou
盲图像质量评估（BIQA）是IQA领域中最具挑战性和最困难的任务之一。鉴于通过字典学习的稀疏表示可以很好地学习图像特征，本文从特征学习的角度提出了一种称为判别特征表示（DFR）的方法，用于噪声污染图像质量评估。 DFR 使用两个由原子组成的子字典，分别具有所需的图像结构和不需要的噪声。通过对两个子词典中与原子相关的稀疏系数的联合评估来量化噪声。该方法使用具有不同类型噪声的公共数据库进行了验证，并与其他最新方法进行了比较。该方法也适用于在不同剂量下采集并通过各种众所周知的算法重建的 CT 图像。
Blind image quality assessment (BIQA) is one of the most challenging and difficult tasks in the field of IQA. Given that sparse representation through dictionary learning can learn the image feature well, this paper proposed a method termed Discriminative Feature Representation (DFR) from the perspective of feature learning for noise contaminated image quality assessment. DFR makes use of two sub-dictionaries composed of atoms featuring desirable image structures and undesirable noise, respectively. Noise is quantified via a joint evaluation of the sparse coefficients related to the atoms in the two sub-dictionaries. The method is validated using public databases with different types of noise, a comparison with other up-to-date methods is provided. The proposed method is also applied to CT images acquired at different-level doses and reconstructed by various well-known algorithms.