Indirect Domain Shift for Single Image Dehazing
Deep convolutional neural networks (CNNs) have been tremendously successful in many high-level computer vision tasks, e.g., image recognition and object detection. Although recent works have shown that it is also possible to learn an end-to-end CNN model for low-level vision tasks, e.g., image dehazing, the resulting performance is still not completely satisfactory. For high-level vision tasks, it suffices to extract specific features and simply express them as very low dimensional vectors, which results in a relatively simple mapping. In contrast, low-level vision tasks require both global understanding of image content and local inference of texture details; as such, the associated mappings are more complicated. In this project, we will explore that the inadequacy of conventional CNN-based dehazing methods and will propose a new method to mapping the hazy images to clear images by indirect way. To address this issue, we will try to add explicit constraints inside a deep CNN model to guide the restoration process.
Voir la description complète du projetJun Chen
The State University of New York at Buffalo
Computer science
Education
McMaster University
Globalink Research Award