| dc.description.abstract | Automatic photo enhancement is one of the long‐standing goals in image processing and computational photography. While a variety of methods have been proposed for manipulating tone and colour, most automatic methods used in practice, operate on the entire image without attempting to take the content of the image into account. In this paper, we present a new framework for automatic photo enhancement that attempts to take local and global image semantics into account. Specifically, our content‐aware scheme attempts to detect and enhance the appearance of human faces, blue skies with or without clouds and underexposed salient regions. A user study was conducted that demonstrates the effectiveness of the proposed approach compared to existing auto‐enhancement tools.Automatic photo enhancement is one of the longstanding goals in image processing and computational photography. While a variety of methods have been proposed for manipulating tone and color, most automatic methods used in practice, operate on the entire image without attempting to take the content of the image into account. In this paper we present a new framework for automatic photo enhancement that attempts to take local and global image semantics into account. Specifically, our content‐aware scheme attempts to detect and enhance the appearance of human faces, blue skies with or without clouds, and underexposed salient regions. A user study was conducted that demonstrates the effectiveness of the proposed approach compared to existing auto‐enhancement tools. | en_US |
