Multimodal Image Translation Algorithm Based on Singular Squeeze-and-Excitation Network

Hangyao Tu, Zheng Wang () and Yanwei Zhao
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Hangyao Tu: School of Computer Science and Technology, Zhejiang University, Hangzhou 310015, China
Zheng Wang: School of Computer and Computational Science, Hangzhou City University, Hangzhou 310015, China
Yanwei Zhao: College of Engineering, Zhejiang University of Technology, Hangzhou 310015, China

Mathematics, 2025, vol. 13, issue 1, 1-19

Abstract: Image-to-image translation methods have advanced from focusing on image-level info to incorporating pixel-level and instance-level details. However, with feature-level constraint, deviation occurs when the network overemphasizes convolutional features, neglecting traditional image feature extraction. To address this, we proposed the multimodal image translation algorithm MASSE based on a Singular Squeeze-and-Excitation Network, combining GANs and SENet. It utilizes SVD features to assist the SENet in managing the scaling degree. The SENet employs SVD to extract features and enhance the Excitation operation to obtain new channel attention weights and form attention feature maps. Then, image content features are refined by combining convolutional and attention feature maps, and style features are obtained by the style generator. Finally, content and style features are combined to generate new style images. Ablation experiments showed the optimal SVD parameter is 128, producing the best translation results. According to FID, MASSE outperforms current methods in generating diverse images.

Keywords: image translation; generative model; singular value decomposition; multimodal images (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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