ICA-based image denoising: A comparative analysis of four classical algorithms

Liang, Kangzhuang; Ye, Jimin

doi:10.1109/icbda.2017.8078728

Cited by 3 publications

(1 citation statement)

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“…The transform domain techniques convert the noisy image into another domain, and then the denoising procedure is applied to coefficients. The primary transform domain techniques are wavelet domain methods [4], spatial frequency domain filtering methods, block-matching, and 3D filtering (BM3D) [5], principal component analysis (PCA) [6], independent component analysis [7] etc. Some of the recent spatial and transform domain methodologies are based on wavelet [8], total variation models [9,10], robust block PCA [11], and fuzzy hysteresis smoothing [12].…”

Section: Introductionmentioning

confidence: 99%

PReLU and edge‐aware filter‐based image denoiser using convolutional neural network

Thakur

Yadav

Gupta

2020

IET Image Processing

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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.

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