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|Title:||Improved wavelet threshold for image de-noising||Authors:||Zhang, Y
|Issue Date:||2019||Publisher:||Frontiers Research Foundation||Source:||Frontiers in neuroscience, 2019, v. 13, no. FEB, 39 How to cite?||Journal:||Frontiers in neuroscience||Abstract:||With the development of communication technology and network technology, as well as the rising popularity of digital electronic products, an image has become an important carrier of access to outside information. However, images are vulnerable to noise interference during collection, transmission and storage, thereby decreasing image quality. Therefore, image noise reduction processing is necessary to obtain higher-quality images. For the characteristics of its multi-analysis, relativity removal, low entropy, and flexible bases, the wavelet transform has become a powerful tool in the field of image de-noising. The wavelet transform in application mathematics has a rapid development. De-noising methods based on wavelet transform is proposed and achieved with good results, but shortcomings still remain. Traditional threshold functions have some deficiencies in image denoising. A hard threshold function is discontinuous, whereas a soft threshold function causes constant deviation. To address these shortcomings, a method for removing image noise is proposed in this paper. First, the method decomposes the noise image to determine the wavelet coefficients. Second, the wavelet coefficient is applied on the high-frequency part of the threshold processing by using the improved threshold function. Finally, the de-noised images are obtained to rebuild the images in accordance with the estimation in the wavelet-based conditions. Experiment results show that this method, discussed in this paper, is better than traditional hard threshold de-noising and soft threshold de-noising methods, in terms of objective effects and subjective visual effects.||URI:||http://hdl.handle.net/10397/81206||ISSN:||1662-4548||EISSN:||1662-453X||DOI:||10.3389/fnins.2019.00039||Rights:||© 2019 Zhang, Ding, Pan and Qin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
The following publication Zhang, Y., Ding, W., Pan, Z., & Qin, J. (2019). Improved Wavelet Threshold for Image De-noising. Frontiers in neuroscience, 13 is available at https://doi.org/10.3389/fnins.2019.00039
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