Subjective and objective quality assessment for image restoration: A critical survey.

Image restoration is the process of restoring latent clear images from degraded images, and it has received substantial attention in various restoration scenarios recently. Contrary to the considerable progress of image restoration algorithms, the quality evaluation for image restoration falls behind, which may hinder the further development of advanced image restoration techniques. So far, only several objective quality metrics have been proposed to evaluate the quality of restored images for specific restoration scenarios, and little work has been dedicated to the applications of image restoration quality metrics. Besides, the performance of these metrics remains an open problem, and an obvious disadvantage of these metrics is that their robustness is quite weak. To bridge this gap, we present this survey paper. First, the difference between the traditional image quality assessment and quality assessment for image restoration was analyzed profoundly. Then, for different image restoration scenarios, this paper provides (i) a comprehensive description of the existing subjective quality databases, (ii) a thorough insight into existing objective quality metrics and (iii) two applications of image restoration quality metrics, namely, parameter selection and benchmarking image restoration algorithms. After that, the experimental results and a detailed performance analysis on (i) quality assessment of restored images, (ii) benchmarking image restoration algorithms and (iii) time complexity are given based on public restored image databases. Finally, the paper outlines the challenges and future trends of subjective and objective quality assessment for image restoration in various aspects. • The difference between the traditional image quality assessment and quality assessment for image restoration was analyzed profoundly. • For different image restoration scenarios, this paper provides a comprehensive description of the existing i) subjective quality databases, ii) objective quality metrics and iii) two applications of image restoration quality metrics. • A detailed performance analysis on i) quality assessment of restored images, ii) benchmarking image restoration algorithms and iii) time complexity are given based on public restored image databases. • The paper outlines the challenges and future trends of subjective and objective quality assessment for image restoration in various aspects. [ABSTRACT FROM AUTHOR]