{"doi":"10.1109/tbme.2025.3640551","title":"ZS-KAN: Zero-Shot Image Denoising With Lightweight Kolmogorov--Arnold Networks","abstract":"OBJECTIVE: Current learning-based image denoising methods have achieved impressive performance. However, their reliance on deep neural architectures and large datasets limits their applicability in data-limited or edge-computing scenarios. Therefore, we aim to develop a lightweight, effective, and efficient neural network for self-supervised image denoising. METHODS: Motivated by the functional approximation capability of Kolmogorov-Arnold networks (KANs), here we present ZS-KAN, a zero-shot denoising approach built upon a hybrid architecture that combines the computational efficiency of convolutional neural networks with the representational flexibility of KANs. RESULTS: Experiments on synthetic and real-world noisy data show that ZS-KAN achieves performance comparable to or even surpassing state-of-the-art zero-shot denoising methods while using only 1% -25% of their parameter counts, significantly reducing model complexity. Visual comparisons indicate that ZS-KAN typically preserves more fine details than competing methods. CONCLUSION: This study demonstrates a strong potential of KANs as a powerful component for zero-shot image denoising. SIGNIFICANCE: The combination of lightweight architecture, self-learning, and computational efficiency leads to the merits of ZS-KAN for real-world deployment, particularly in medical imaging workflows.","journal":"IEEE Transactions on Biomedical Engineering","year":2025,"id":584208,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9569,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":277821,"name":"Ge Wang","orcid":"0000-0002-2656-7705","position":1,"is_corresponding":false},{"id":1497097,"name":"Jianxu Wang","orcid":"0009-0009-2113-0668","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:59:11.978098Z","pmid":"41343303","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}