{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:34:53Z","timestamp":1775745293654,"version":"3.50.1"},"reference-count":36,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2019,5]]},"abstract":"<jats:p> Nowadays, Internet of things not only brings promising opportunities but also faces a lot of challenges. It attracts a lot of researchers\u2019 attention and has important economic and social values. Internet of things plays a key role in the big data processing, especially in image field. Image de-noising still is a key problem in image pre-processing. Considering a given noisy image, the selection of thresholds should significantly affect the quality of the de-noising image. Although the state-of-the-art wavelet image de-noising methods perform better than other de-noising methods, they are not very effective for de-noising with different noises and with redundancy convergence time, sometimes. To mitigate the poor effect of traditional de-noising methods, this article proposes a new wavelet soft threshold based on the Chi-square distribution-Kernel method under the Internet of things big data environment. The new method alternates three minimization steps. First, the Chi-square distribution-Kernel model is constructed to find the customized threshold that corresponds to the de-noised image. Second, a freedom degree is considered, which is related to the customized wavelet coefficient of the Chi-square distribution-Kernel to be thresholded for image de-noising. Here, noisy image is first decomposed into many levels to obtain different frequency bands and the soft thresholding method based on Chi-square distribution-Kernel method is used to remove the noisy coefficients, by fixing the optimum threshold value using the proposed method. Third, the wavelet soft thresholding based on Chi-square distribution-Kernel method is adopted to handle the image de-noising, and a significant improvement is obtained by a specially developed Chi-square distribution-Kernel method. Finally, the experimental results illustrate that this computationally scalable algorithm achieves state-of-the-art de-noising performance in terms of peak signal-to-noise ratio, normalized mean square error, structural similarity, and subjective visual quality. It also shows a consistent accuracy, edge preservation, and detailed retention improvement compared to the classic de-noising algorithms. <\/jats:p>","DOI":"10.1177\/1550147719847133","type":"journal-article","created":{"date-parts":[[2019,5,3]],"date-time":"2019-05-03T10:50:57Z","timestamp":1556880657000},"page":"155014771984713","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":11,"title":["CSDK: A Chi-square distribution-Kernel method for image de-noising under the Internet of things big data environment"],"prefix":"10.1177","volume":"15","author":[{"given":"Lin","family":"Teng","sequence":"first","affiliation":[{"name":"College of Software, Shenyang Normal University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1230-4007","authenticated-orcid":false,"given":"Hang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Software, 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