CSHNet: A Novel Information Asymmetric Image Translation Method
Journal:
arXiv
Published Date:
Jan 17, 2025
Abstract
Despite advancements in cross-domain image translation, challenges persist in
asymmetric tasks such as SAR-to-Optical and Sketch-to-Instance conversions,
which involve transforming data from a less detailed domain into one with
richer content. Traditional CNN-based methods are effective at capturing fine
details but struggle with global structure, leading to unwanted merging of
image regions. To address this, we propose the CNN-Swin Hybrid Network
(CSHNet), which combines two key modules: Swin Embedded CNN (SEC) and CNN
Embedded Swin (CES), forming the SEC-CES-Bottleneck (SCB). SEC leverages CNN's
detailed feature extraction while integrating the Swin Transformer's structural
bias. CES, in turn, preserves the Swin Transformer's global integrity,
compensating for CNN's lack of focus on structure. Additionally, CSHNet
includes two components designed to enhance cross-domain information retention:
the Interactive Guided Connection (IGC), which enables dynamic information
exchange between SEC and CES, and Adaptive Edge Perception Loss (AEPL), which
maintains structural boundaries during translation. Experimental results show
that CSHNet outperforms existing methods in both visual quality and performance
metrics across scene-level and instance-level datasets. Our code is available
at: https://github.com/XduShi/CSHNet.
