دورية أكاديمية
A Second-Order Method for Removing Mixed Noise from Remote Sensing Images
| العنوان: | A Second-Order Method for Removing Mixed Noise from Remote Sensing Images |
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| المؤلفون: | Ying Zhou, Chao Ren, Shengguo Zhang, Xiaoqin Xue, Yuanyuan Liu, Jiakai Lu, Cong Ding |
| المصدر: | Sensors, Vol 23, Iss 17, p 7543 (2023) |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Chemical technology |
| مصطلحات موضوعية: | remote sensing image, mixed noise, DnCNN, adaptive median filtering, nearest neighbor pixel weighted median, Chemical technology, TP1-1185 |
| الوصف: | Remote sensing image denoising is of great significance for the subsequent use and research of images. Gaussian noise and salt-and-pepper noise are prevalent noises in images. Contemporary denoising algorithms often exhibit limitations when addressing such mixed noise scenarios, manifesting in suboptimal denoising outcomes and the potential blurring of image edges subsequent to the denoising process. To address the above problems, a second-order removal method for mixed noise in remote sensing images was proposed. In the first stage of the method, dilated convolution was introduced into the DnCNN (denoising convolutional neural network) network framework to increase the receptive field of the network, so that more feature information could be extracted from remote sensing images. Meanwhile, a DropoutLayer was introduced after the deep convolution layer to build the noise reduction model to prevent the network from overfitting and to simplify the training difficulty, and then the model was used to perform the preliminary noise reduction on the images. To further improve the image quality of the preliminary denoising results, effectively remove the salt-and-pepper noise in the mixed noise, and preserve more image edge details and texture features, the proposed method employed a second stage on the basis of adaptive median filtering. In this second stage, the median value in the original filter window median was replaced by the nearest neighbor pixel weighted median, so that the preliminary noise reduction result was subjected to secondary processing, and the final denoising result of the mixed noise of the remote sensing image was obtained. In order to verify the feasibility and effectiveness of the algorithm, the remote sensing image denoising experiments and denoised image edge detection experiments were carried out in this paper. When the experimental results are analyzed through subjective visual assessment, images denoised using the proposed method exhibit clearer and more natural details, and they effectively retain edge and texture features. In terms of objective evaluation, the performance of different denoising algorithms is compared using metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR), and mean structural similarity index (MSSIM). The experimental outcomes indicate that the proposed method for denoising mixed noise in remote sensing images outperforms traditional denoising techniques, achieving a clearer image restoration effect. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 23177543 1424-8220 |
| العلاقة: | https://www.mdpi.com/1424-8220/23/17/7543Test; https://doaj.org/toc/1424-8220Test |
| DOI: | 10.3390/s23177543 |
| الوصول الحر: | https://doaj.org/article/f7d88b0442c64bb1b8919425a06f9e54Test |
| رقم الانضمام: | edsdoj.f7d88b0442c64bb1b8919425a06f9e54 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 23177543 14248220 |
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| DOI: | 10.3390/s23177543 |