Hongjian He

Guanjie Huang, Hongjian He, Xiang Li et al.

Currently, it is hard to compare and evaluate different style transfer algorithms due to chaotic definitions of style and the absence of agreed objective validation methods in the study of style transfer. In this paper, a novel approach, the Unified Style Transfer (UST) model, is proposed. With the introduction of a generative model for internal style representation, UST can transfer images in two approaches, i.e., Domain-based and Image-based, simultaneously. At the same time, a new philosophy based on the human sense of art and style distributions for evaluating the transfer model is presented and demonstrated, called Statistical Style Analysis. It provides a new path to validate style transfer models' feasibility by validating the general consistency between internal style representation and art facts. Besides, the translation-invariance of AdaIN features is also discussed.

Zhiyang Lu, Jun Li, Zheng Li et al.

Quantitative Susceptibility Mapping (QSM) is a new phase-based technique for quantifying magnetic susceptibility. The existing QSM reconstruction methods generally require complicated pre-processing on high-quality phase data. In this work, we propose to explore a new value of the high-pass filtered phase data generated in susceptibility weighted imaging (SWI), and develop an end-to-end Cross-connected $Ψ$-Net (C$Ψ$-Net) to reconstruct QSM directly from these phase data in SWI without additional pre-processing. C$Ψ$-Net adds an intermediate branch in the classical U-Net to form a $Ψ$-like structure. The specially designed dilated interaction block is embedded in each level of this branch to enlarge the receptive fields for capturing more susceptibility information from a wider spatial range of phase images. Moreover, the crossed connections are utilized between branches to implement a multi-resolution feature fusion scheme, which helps C$Ψ$-Net capture rich contextual information for accurate reconstruction. The experimental results on a human dataset show that C$Ψ$-Net achieves superior performance in our task over other QSM reconstruction algorithms.