{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T16:56:02Z","timestamp":1772211362821,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jilin Provincial Department of Education Science and Technology Research Project","award":["JJKH20230808KJ"],"award-info":[{"award-number":["JJKH20230808KJ"]}]},{"name":"Jilin Provincial Science and Technology Development Plan Project","award":["20210201088GX"],"award-info":[{"award-number":["20210201088GX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Addressing the challenges of composite noise (speckle noise, thermal noise, and random pulse interference) and non-stationarity in laser Doppler flow (LDF) signal processing, as well as the technical limitation of traditional threshold methods in balancing noise suppression and signal fidelity, this study proposes an adaptive denoising algorithm integrating temporal noise perception and discrete wavelet transform (DWT). A composite noise model is first established to characterize the interference. The signal undergoes a five-level DWT decomposition, where a local energy detection mechanism distinguishes signal-dominant from noise-dominant regions. An SNR-driven dynamic thresholding strategy is generated by combining inter-layer adaptive allocation with coefficient-level local weighting, followed by processing with an improved smoothing function to effectively suppress reconstruction artifacts. Simulations at a 1 dB input signal-to-noise ratio (SNR) yielded a 15.45 dB output SNR and a 0.05634 root mean square error (RMSE), outperforming traditional wavelet methods and modern benchmarks such as local variance and variational mode decomposition (VMD). Applied to a practical signal from an isolated vascular phantom with an initial SNR of \u22121.04 dB, the algorithm achieved a 13.86 dB output SNR and a 0.00258 RMSE. Results confirm the algorithm\u2019s effectiveness for high-fidelity waveform capture in complex noise environments, offering a robust solution for vascular hemodynamic monitoring<\/jats:p>","DOI":"10.3390\/s26051500","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:52:38Z","timestamp":1772207558000},"page":"1500","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Denoising Methods for Laser Doppler Blood Flow Signals Based on Time-Domain Noise Perception and DWT"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2116-5667","authenticated-orcid":false,"given":"Quanxin","family":"Sun","sequence":"first","affiliation":[{"name":"School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5111-533X","authenticated-orcid":false,"given":"Jie","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Guo","sequence":"additional","affiliation":[{"name":"CRRC Changchun Railway Vehicles Co., Ltd., Changchun 130062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aoyan","family":"Guo","sequence":"additional","affiliation":[{"name":"CRRC Changchun Railway Vehicles Co., Ltd., Changchun 130062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"ref_1","unstructured":"Terjung, R. (2020). Human Skin Microcirculation. Comprehensive Physiology, Wiley-Blackwell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104373","DOI":"10.1016\/j.mvr.2022.104373","article-title":"Laser Doppler Flowmetry Evaluation of Skin Microvascular Endothelial Function in Patients with Metabolic Syndrome","volume":"142","author":"Girkantaite","year":"2022","journal-title":"Microvasc. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.ejvsvf.2025.09.003","article-title":"Feasibility of Portable Laser Doppler Flowmeter for Foot Blood Flow Assessment in Patients with Chronic Limb Threatening Ischaemia","volume":"64","author":"Tezuka","year":"2025","journal-title":"EJVES Vasc. Forum"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"016109","DOI":"10.1063\/5.0182670","article-title":"A Portable Optical Pulsatile Flowmetry Demonstrates Strong Clinical Relevance for Diabetic Foot Perfusion Assessment","volume":"8","author":"Bi","year":"2024","journal-title":"APL Bioeng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"055003","DOI":"10.1117\/1.JBO.25.5.055003","article-title":"Fast Pulsatile Blood Flow Measurement in Deep Tissue Through a Multimode Detection Fiber","volume":"25","author":"Bi","year":"2020","journal-title":"J. Biomed. Opt."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Stefanovska, A., and McClintock, P.V.E. (2021). Complexity-Based Analysis of Microvascular Blood Flow in Human Skin. Physics of Biological Oscillators, Springer. Understanding Complex Systems.","DOI":"10.1007\/978-3-030-59805-1"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"326","DOI":"10.2478\/raon-2024-0042","article-title":"Laser Speckle Contrast Imaging of Perfusion in Oncological Clinical Applications: A Literature Review","volume":"58","author":"Hren","year":"2024","journal-title":"Radiol. Oncol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., Zhang, B., Qi, Y., and Chen, S. (2024). An Improved Vital Signal Extraction Method Based on Laser Doppler Effect. Sensors, 24.","DOI":"10.3390\/s24217027"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kralj, L., and Lenasi, H. (2023). Wavelet Analysis of Laser Doppler Microcirculatory Signals: Current Applications and Limitations. Front. Physiol., 13.","DOI":"10.3389\/fphys.2022.1076445"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"120701","DOI":"10.3788\/LOP54.120701","article-title":"Processing of Laser Doppler Velocity Signal Based on Wavelet Threshold De-Noising","volume":"54","author":"Liu","year":"2017","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_11","first-page":"129","article-title":"Laser Doppler Vibration Signal Processing Based on Wavelet Denoising","volume":"46","author":"Tan","year":"2022","journal-title":"Laser Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"25774","DOI":"10.1364\/OE.461116","article-title":"Coherent Doppler Wind Lidar Signal Denoising Adopting Variational Mode Decomposition Based on Honey Badger Algorithm","volume":"30","author":"Zhang","year":"2022","journal-title":"Opt. Express"},{"key":"ref_13","first-page":"1714008","article-title":"Laser Doppler Signal Processing Based on Hybrid Convolution Window","volume":"60","author":"Chen","year":"2023","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107835","DOI":"10.1016\/j.compbiomed.2023.107835","article-title":"Accurate Wavelet Thresholding Method for ECG Signals","volume":"169","author":"Yu","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s10103-007-0524-0","article-title":"Review of Methodological Developments in Laser Doppler Flowmetry","volume":"24","author":"Rajan","year":"2009","journal-title":"Lasers Med. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1364\/AO.20.002097","article-title":"Model for Laser Doppler Measurements of Blood Flow in Tissue","volume":"20","author":"Bonner","year":"1981","journal-title":"Appl. Opt."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107135","DOI":"10.1016\/j.optlaseng.2022.107135","article-title":"Numerical Simulation and Characterization of Speckle Noise for Laser Doppler Vibrometer on Moving Platforms (LDVom)","volume":"158","author":"Jin","year":"2022","journal-title":"Opt. Lasers Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"415","DOI":"10.3233\/CH-131797","article-title":"Preliminary Study of Laser Doppler Perfusion Signal by Wavelet Transform in Patients with Critical Limb Ischemia Before and After Revascularization","volume":"58","author":"Ticcinelli","year":"2014","journal-title":"Clin. Hemorheol. Microcirc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A Theory for Multiresolution Signal Decomposition: The Wavelet Representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","first-page":"2098","article-title":"Communication Signal Denoising Algorithm Based on Wavelet Energy Ratio and Improved Threshold Function","volume":"47","author":"Lian","year":"2025","journal-title":"Syst. Eng. Electron."},{"key":"ref_21","first-page":"1264","article-title":"Application of Two-parameter Threshold Function Based on Energy Thresholds in Denoising of Physiological Signal","volume":"45","author":"Zhao","year":"2024","journal-title":"Acta Armamentarii"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/5\/1500\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:56:34Z","timestamp":1772207794000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/5\/1500"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,27]]},"references-count":21,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["s26051500"],"URL":"https:\/\/doi.org\/10.3390\/s26051500","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,27]]}}}