Abstract: A method for enhancing a near-infrared fluorescence image includes providing a NIR fluorescence image produced by detecting light in the NIR-I or NIR-IIa windows emitted by fluorophores; and inputting the NIR fluorescence image to a convolutional neural network to produce as output a translated image, where the convolutional neural network is trained using a set of NIR-I or NIR-IIa fluorescence images and a set of NIR-IIb fluorescence images. Preferably, the convolutional neural network is a U-Net.

Abstract: Described are an encoding method and system for ROAs. The encoding method includes the following steps: given a set of authorized IP prefixes an AS which are maintained with an IP address trie. By specifying a sequence of hanging levels on the IP address trie, it is divided into a set of non-overlapping sub-trees, each rooted at a hanging level. A node on a hanging level uniquely defines a sub-tree rooted at it, whose prefix can be encoded as the identifier of this sub-tree. All authorized prefixes covered by a sub-tree can be encoded into a bitmap of 2h bits, where h is the height of this sub-tree. Thus, the set of authorized IP prefixes of an AS is encoded into several tuples (identifier, bitmap).

Abstract: A method for enhancing a near-infrared fluorescence image includes providing a NIR fluorescence image produced by detecting light in the NIR-I or NIR-IIa windows emitted by fluorophores; and inputting the NIR fluorescence image to a convolutional neural network to produce as output a translated image, where the convolutional neural network is trained using a set of NIR-I or NIR-IIa fluorescence images and a set of NIR-IIb fluorescence images. Preferably, the convolutional neural network is a U-Net.