Mixed Gaussian-impulse noise reduction from images using convolutional neural network

Islam, Mohammad Tariqul; Rahman, S.M.M.; Ahmad, M. Omair; Swamy, M.N.S.

doi:10.1016/j.image.2018.06.016

Cited by 54 publications

(51 citation statements)

References 38 publications

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“…In recent years, Deep Learning (DL) has achieved great progress in object recognition (Tong et al, 2019; Xie et al, 2019), prediction (Yan et al, 2018; Hao et al, 2019), speech analysis (Cummins et al, 2018), noise reduction (Islam et al, 2018), monitoring (Li et al, 2018; Wang et al, 2018), medicine (Raja et al, 2015; Safdar et al, 2018), the recommendation system (Zhang and Liu, 2014), biometrics (Xing et al, 2017) and so on. Traditionally, DL consists of multiple layers of non-linear processing units to obtain the features.…”

Section: Methodsmentioning

confidence: 99%

Cerebral Micro-Bleeding Detection Based on Densely Connected Neural Network

et al. 2019

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Cerebral micro-bleedings (CMBs) are small chronic brain hemorrhages that have many side effects. For example, CMBs can result in long-term disability, neurologic dysfunction, cognitive impairment and side effects from other medications and treatment. Therefore, it is important and essential to detect CMBs timely and in an early stage for prompt treatment. In this research, because of the limited labeled samples, it is hard to train a classifier to achieve high accuracy. Therefore, we proposed employing Densely connected neural network (DenseNet) as the basic algorithm for transfer learning to detect CMBs. To generate the subsamples for training and test, we used a sliding window to cover the whole original images from left to right and from top to bottom. Based on the central pixel of the subsamples, we could decide the target value. Considering the data imbalance, the cost matrix was also employed. Then, based on the new model, we tested the classification accuracy, and it achieved 97.71%, which provided better performance than the state of art methods.

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Section: Methodsmentioning

confidence: 99%

Cerebral Micro-Bleeding Detection Based on Densely Connected Neural Network

et al. 2019

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“…Convolution stage consists of convolution layer, ReLU layer and max‐pooling layer. The four‐stage CNN is used because by experiments, it has been shown that the increase in PSNR is less than 0.5% due to the inclusion of a new convolution layer after four [32]. Further, it is observed that increasing the number of convolution layers increases the computational load significantly.…”

Section: Different Cnn Models For Image Denoisingmentioning

confidence: 99%

“…In this CNN [32], in the first convolution stageconvolution filter is followed by ReLU and max‐pool layer. The second andthird stages consist of convolution filter and ReLU; the last stage onlyconsists of the convolution filter.…”

Section: Different Cnn Models For Image Denoisingmentioning

confidence: 99%

“…Mixed noise occurs when there are faulty sensors due to temperature fluctuations or during transmission process. The CNN model [32] for mixed noise reduction can be used for remote sensing images including real desert seismic record which are prone to mixed noise as camera sensors are placed in harsh geographical conditions. Moreover, real world noisy datasets, e.g.…”

Section: Real‐life Applicationsmentioning

confidence: 99%

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State‐of‐art analysis of image denoising methods using convolutional neural networks

2019

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Convolutional neural networks (CNNs) are deep neural networks that can be trained on large databases and show outstanding performance on object classification, segmentation, image denoising etc. In the past few years, several image denoising techniques have been developed to improve the quality of an image. The CNN based image denoising models have shown improvement in denoising performance as compared to non‐CNN methods like block‐matching and three‐dimensional (3D) filtering, contemporary wavelet and Markov random field approaches etc. which had remained state‐of‐the‐art for years. This study provides a comprehensive study of state‐of‐the‐art image denoising methods using CNN. The literature associated with different CNNs used for image restoration like residual learning based models (DnCNN‐S, DnCNN‐B, IDCNN), non‐locality reinforced (NN3D), fast and flexible network (FFDNet), deep shrinkage CNN (SCNN), a model for mixed noise reduction, denoising prior driven network (PDNN) are reviewed. DnCNN‐S and PDNN remove Gaussian noise of fixed level, whereas DnCNN‐B, IDCNN, NN3D and SCNN are used for blind Gaussian denoising. FFDNet is used for spatially variant Gaussian noise. The performance of these CNN models is analysed on BSD‐68 and Set‐12 datasets. PDNN shows the best result in terms of PSNR for both BSD‐68 and Set‐12 datasets.

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“…The removal of impulse noise is decomposed into two steps: the identification of noisy pixels and the recovery of the true image. To further improve the denoising performance, machine learning [10] and neural networks [11–12] are introduced to help remove the impulse noise. First, machine learning or neural networks are used to improve the accuracy of the recognition of noisy pixels.…”

Section: Introductionmentioning

confidence: 99%

A novel method to remove impulse noise from atomic force microscopy images based on Bayesian compressed sensing

Zhang¹,

Li²,

Song³

et al. 2019

Beilstein J. Nanotechnol.

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A novel method based on Bayesian compressed sensing is proposed to remove impulse noise from atomic force microscopy (AFM) images. The image denoising problem is transformed into a compressed sensing imaging problem of the AFM. First, two different ways, including interval approach and self-comparison approach, are applied to identify the noisy pixels. An undersampled AFM image is generated by removing the noisy pixels from the image. Second, a series of measurement matrices, all of which are identity matrices with some rows removed, are constructed by recording the position of the noise-free pixels. Third, the Bayesian compressed sensing reconstruction algorithm is applied to recover the image. Different from traditional compressed sensing reconstruction methods in AFM, each row of the AFM image is reconstructed separately in the proposed method, which will not reduce the quality of the reconstructed image. The denoising experiments are conducted to demonstrate that the proposed method can remove the impulse noise from AFM images while preserving the details of the image. Compared with other methods, the proposed method is robust and its performance is not influenced by the noise density in a certain range.

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Mixed Gaussian-impulse noise reduction from images using convolutional neural network

Cited by 54 publications

References 38 publications

Cerebral Micro-Bleeding Detection Based on Densely Connected Neural Network

Cerebral Micro-Bleeding Detection Based on Densely Connected Neural Network

State‐of‐art analysis of image denoising methods using convolutional neural networks

A novel method to remove impulse noise from atomic force microscopy images based on Bayesian compressed sensing

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