An image quality improvement method based on visual attention model

In this paper, we proposed an image quality improvement method based on visual attention model. The proposed scheme is composed of three parts: pre-processing, visual attention model generation, and exposure correction. To extract more visual cues for visual attention model generation, a pre-processing is used to modify the input image. After preprocessing, facial and non-facial cues are measured to generate visual attention maps. Based on visual attention maps, an exposure correction algorithm is utilized to adjust the exposure level of the input image and then create several intermediate results. After fusing intermediate results, a synthesized image with good visual quality can be obtained. The experimental results demonstrate that the proposed method can deal with images with low and high exposures. The results also show that the proposed scheme outperforms existing methods.