Paper Info
Title
Thin Semantics Enhancement via High-Frequency Priori Rule for Thin Structures Segmentation
Abstract
Receptive field-based segmentation models represent features in receptive fields having weak perception for thin semantics in thin structures segmentation, due to the challenges in small local size and large global variation. High-frequency (HiFe) components have strong thin perception ability and is stable for global variation, but its weak adaptability limits its direct application. We propose a HiFe priori rule which enables the network to adaptively extract and fuse HiFe components, enhancing the thin semantics and making the network naturally prefer thin structures for their segmentation. We further propose High-Frequency Semantics Enhancement Network (HiFeNet) based on our HiFe priori rule, boosting the SOTA methods in thin structures segmentation: 1) Our Deep High Frequency (DHiFe) block learns to extract task-dependent HiFe components and adds them to feature maps, achieving great perception of thin structures. 2) Our Latent Residual Denoising (LRD) block progressively weakens task-independent features via hierarchical residuals and learns to fuse HiFe components back to feature maps, further enhancing the thin semantics and weakening the interference of global variation. Extensive experiments on the retinal vessel [1], [2], [3] and Massachusetts road [4] segmentation datasets show great superiority of our HiFeNet.
Year
DOI
Venue
2021
10.1109/ICDM51629.2021.00128
2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021)
Keywords
DocType
ISSN
Thin structures, Thin semantics enhancement, High-frequency priori rule, Discrete wavelet transform
Conference
1550-4786
Citations 
PageRank 
References 
0
0.34
0
Authors
8
Name
Order
Citations
PageRank
Yuting He154.45
Rongjun Ge2155.09
Jiasong Wu36013.26
J L Coatrieux427351.89
Huazhong Shu594090.05
Yang Chen600.34
Guanyu Yang72713.48
Shuo Li801.01