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Content Provider | IET Digital Library |
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Author | Thakur, Rini Smita Yadav, Ram Narayan Gupta, Lalita |
Abstract | Convolutional neural networks (CNNs) based on the discriminative learning model have been widely used for image denoising. In this study, a feed-forward denoising CNN (DnCNN) with a parametric rectified linear unit (PReLU) is used to improve the denoising performance. PReLU enhances the model fitting of the DnCNN network without affecting computational cost. This network learns the leaky parameter of negative inputs in an activation function and therefore finds a proper slope in a negative direction. The proposed denoising network is based on residual learning, which comprises repeated convolutional and PReLU units along with batch normalisation. Residual learning with batch normalisation accelerates the network training, which can be used for blind Gaussian denoising. In this network, feature maps are processed by principal component analysis and transferred to subsequent convolution layers. An adaptive bilateral filter further processes the output image of the proposed CNN for image smoothening and sharpening. The mean and variance of the Gaussian kernel of adaptive filter vary from pixel to pixel. The performance of this network is analysed on BSD-68 and Set-12 datasets, and it exhibits an improvement in peak signal-to-noise ratio and structural similarity index metric and visual representation over other state-of-the-art methods. |
Starting Page | 3869 |
Ending Page | 3879 |
Page Count | 11 |
ISSN | 17519659 |
Volume Number | 14 |
e-ISSN | 17519667 |
Issue Number | Issue 15, Dec (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/14/15 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0717 |
Journal | IET Image Processing |
Publisher Date | 2020-12-04 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Adaptive Bilateral Filter Adaptive Filter Batch Normalisation Blind Gaussian Denoising CNN Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Denoising Performance Discriminative Learning Model DnCNN Network Edge-aware Filter-based Image Denoiser Filtering Method in Signal Processing Filtering Theory Gaussian Processes Image Denoising Image Resolution Image Smoothening Learning in AI Negative Input Network Training Optical, Image And Video Signal Processing Output Image Parametric Rectified Linear Unit PReLU Principal Component Analysis Residual Learning Sharpening Subsequent Convolution Layer |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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