Due to unfavorable environmental conditions such as lack of lighting, poor visual quality in images and videos may make intelligent image/video systems unstable. This means that visual quality enhancement plays an important role in image/video processing, computer vision, and pattern recognition. In this paper, we propose a video quality enhancement scheme based on visual attention model and multi-level exposure correction. To this end, the proposed scheme is composed of four parts: pre-processing, visual attention model generation, multi-level exposure correction, and temporal filtering. To extract more visual cues for visual attention model generation, a pre-processing is used to modify each frame. After preprocessing, facial and non-facial cues are measured to generate visual attention maps of each frame. On the basis of visual attention maps, a multi-level exposure correction algorithm is utilized to adjust the exposure level of each frame and then create several intermediate results. After fusing intermediate results, a synthesized image with good visual quality can be obtained. To avoid flicker effect, a temporal filter is exploited to make the variance of the exposure level small in the temporal domain. To evaluate the performance of the proposed scheme, some images/videos captured by mobile phones and digital cameras are tested. The experimental results show that the proposed scheme can effectively deal with the images/videos with low and high exposure levels. The results also demonstrate that the proposed scheme outperforms some existing methods in terms of visual quality.
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.
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