CSDK: A Chi-square distribution-Kernel method for image de-noising under the Internet of things big data environment

Nowadays, Internet of things not only brings promising opportunities but also faces a lot of challenges. It attracts a lot of researchers’ 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.