Evaluating the quality of super-resolved images for face recognition
The widespread use of super-resolution methods, in a variety of applications such as surveillance has led to an increasing need for or quality assessment measures. The current quality measures aim to compare different fusion methods by assessing the quality of the fused images. They consider the information transferred between the super-resolved image and input images only. In this paper, we propose an objective quality evaluation algorithm for super-resolved images, which focuses on evaluating the quality of super-resolved images that are constructed from different conditions of input images. The proposed quality evaluation method combines both the relationship between the super-resolved image and the input images, and the relationship between the input images. Using the proposed measure, the quality of the super-resolved face images constructed from videos are evaluated under different conditions, including the variation of pose, lighting, facial expressions and the number of input images.