Special Topics in Multi-Source Feature Fusion
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App. Res. of Comp. Printed Article
Vol. 43, 2026 No. 3
696-703

WKCN:adaptive image description algorithm based on wavelet-KAN collaborative optimization

He Shipeng1
Zheng Hao1,2
1. School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine/Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing 210023, China
2. School of Information and Engineering, Nanjing Xiaozhuang University, Nanjing 211171, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0256

Abstract

This study addressed key challenges in image captioning, including inadequate multi-scale texture representation, redundant feature fusion, and limited dynamic semantic modeling. This paper proposed a novel algorithm named WKCN based on wavelet-KAN collaborative optimization. It designed the wavelet-KAN multi-scale nonlinear enhancement(WKMNE) modu-le to decompose image features using Daubechies-4 wavelet bases and enhance textures via B-spline interpolation in KAN. A KAN-based adaptive feature fusion(KAN-AFF) mechanism dynamically generated spatial and channel weights to integrate global features from ResNet50 and frequency-domain features from WKMNE. Finally, a KAN-enhanced dynamic decoder(KED) replaced the static feed-forward network in Transformer with a learnable KAN activation module to strengthen semantic mapping. Experiments on the MSCOCO dataset show that WKCN achieves optimal scores on BLEU-1(81.1), ROUGE-L(59.1) and CIDEr(133.6). Ablation studies confirme the synergy between multi-scale feature extraction and dynamic decoding. Hyperparameter analysis verifies the rationality of parameter selection. Cross-dataset tests on Flickr30k and NoCaps demonstrate strong generalization capability. Visualization analyses illustrate the model's effectiveness intuitively. This work provides a verifiable nonlinear optimization paradigm for cross-modal semantic generation tasks.

Foundation Support

国家自然科学基金资助项目(61976118)
江苏省研究生科研创新计划资助项目(KYCX25_2266)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0256
Publish at: Application Research of Computers Printed Article, Vol. 43, 2026 No. 3
Section: Special Topics in Multi-Source Feature Fusion
Pages: 696-703
Serial Number: 1001-3695(2026)03-007-0696-08

Publish History

[2025-11-17] Accepted Paper
[2026-03-05] Printed Article

Cite This Article

何世鹏, 郑豪. WKCN:基于小波-KAN协同优化的自适应图像描述算法 [J]. 计算机应用研究, 2026, 43 (3): 696-703. (He Shipeng, Zheng Hao. WKCN:adaptive image description algorithm based on wavelet-KAN collaborative optimization [J]. Application Research of Computers, 2026, 43 (3): 696-703. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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