{"id":6173,"date":"2026-05-10T06:35:13","date_gmt":"2026-05-09T22:35:13","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=6173"},"modified":"2026-07-03T23:41:51","modified_gmt":"2026-07-03T15:41:51","slug":"bent-identity-based-cnn-for-image-denoising","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=bent-identity-based-cnn-for-image-denoising","title":{"rendered":"Bent Identity-based CNN for Image Denoising"},"content":{"rendered":"\n
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<\/i> 2020<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 23, Issue 3<\/a><\/p>\n<\/div>\n\n\n\n

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Qiufeng Fan1<\/sup><\/i><\/a>, Fanbo Hou1<\/sup><\/i><\/a>, and Feng Shi1<\/sup><\/p>\n\n\n\n

1<\/sup>School of Electronic Information Electrical Engineering, Anyang Institute of Technology Anyang 455000,China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0November 20, 2019
Accepted:\u00a0March 31, 2020
Publication Date:\u00a0May 10, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Image Marilyn denoising results and experiment contrast. (a) DMS; (b) NWT; (c) PNM; (d) EGL; (e) NLG; (f) Proposed.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202009_23(3).0019<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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In the process of image acquisition and transmission, the image will be polluted by noise. Therefore, we propose a bent identity-based convolutional neural network (BICNN) model. The model is a full convolu-tional network model with a depth of 30 layers, consisting of six feature extraction modules (FEM) and skip connection. Skip connection combines the output features of the first convolution layer with the output features of each FEM in series to guarantee the full extraction of image\u2019s features. Then we adopt the residual learning to alleviate the gradient disappearance and improve the convergence speed so as to ensure that the nonlinear mapping acquired by the trained denoising model is image noise. Bent identity is selected as the activation function, which has soft saturation and the output mean is close to zero, which can enhance the robustness of the model against input noise and accelerate the convergence of the model. Our extensive experiments demonstrate that our BICNN model can not only exhibit high effec-tiveness in several general image denoising tasks, but also make it highly attractive for practical denoising applications.<\/p>\n\n\n\n

Keywords:\u00a0Image denoising; Bent identity activation function; convolutional neural network; FEM<\/em><\/p>\n\n\n\n

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