The ANN based detector to remove random-valued impulse noise in images

Turkmen, Ilke

doi:10.1016/j.jvcir.2015.10.011

Cited by 48 publications

(32 citation statements)

References 42 publications

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“…Besides model-based methods, some switching filters also use classical machine learning approaches for impulses detection such as Support Vector Machines [32] and fully connected neural networks [33]- [36]. The detected impulses are restored using a median of uncorrupted pixels [33], [35], adaptive and iterative mean filters [34] or the edge-preserving regularization method [36].…”

Section: Introductionmentioning

confidence: 99%

Deep learning for impulsive noise removal in color digital images

Radlak

Maliński

Smołka

2019

Real-Time Image Processing and Deep Learning 2019

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Noise reduction is one the most important and still active research topic in low-level image processing due to its high impact on object detection and scene understanding for computer vision systems. Recently, we can observe a substantial increase of interest in the application of deep learning algorithms in many computer vision problems due to its impressive capability of automatic feature extraction and classification. These methods have been also successfully applied in image denoising, significantly improving the performance, but most of the proposed approaches were designed for Gaussian noise suppression. In this paper, we present a switching filtering design intended for impulsive noise removal using deep learning. In the proposed method, the impulses are identified using a novel deep neural network architecture and noisy pixels are restored using the fast adaptive mean filter. The performed experiments show that the proposed approach is superior to the state-of-the-art filters designed for impulsive noise removal in digital color images.

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Section: Introductionmentioning

confidence: 99%

Deep learning for impulsive noise removal in color digital images

Radlak

Maliński

Smołka

2019

Real-Time Image Processing and Deep Learning 2019

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“…On the other hand, some denoising methods have lower complexity. For example, in previous studies, a patch oriented approach based on the image texture is used for noise detection. Turkmen designed a multilayer perceptron model for detection of noisy pixels .…”

Section: Introductionmentioning

confidence: 99%

“…For example, in previous studies, a patch oriented approach based on the image texture is used for noise detection. Turkmen designed a multilayer perceptron model for detection of noisy pixels . To train this model, two features are extracted from the image patches including rank ordered absolute difference (ROAD) and rank ordered logarithmic difference (ROLD).…”

Section: Introductionmentioning

confidence: 99%

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Real‐time removal of impulse noise from MR images for radiosurgery applications

HosseinKhani

Hajabdollahi

Karimi

et al. 2019

Circuit Theory & Apps

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Summary In the recent years, image processing techniques are used as a tool to improve detection and diagnostic capabilities in the medical applications. Among these techniques, medical image enhancement algorithms play an essential role in the removal of the noise, which can be produced by medical instruments and during image transfer. Impulse noise is a major type of noise, which is produced by medical imaging systems, such as MRI, computed tomography (CT), and angiography instruments. An embeddable hardware module, which can denoise medical images before and during surgical operations, could be very helpful. In this paper, an accurate algorithm is proposed for real‐time removal of impulse noise in medical images. Our algorithm categorizes all image blocks into three types of edge, smooth, and disordered areas. A different reconstruction method is applied to each category of blocks for noise removal. The proposed method is tested on MR images. Simulation results show acceptable denoising accuracy for various levels of noise. Also, an field programmable gate array (FPGA) implementation of our denoising algorithm shows acceptable hardware resource utilization. Hence, the algorithm is suitable for embedding in medical hardware instruments such as radiosurgery devices.

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“…In deterministic image representation, the image pixels are defined in terms of a certain function, possibly unknown, while, in statistical image representation, the images are specified in probabilistic terms as means, covariances, and higher degree moments [5][6][7]. In the past years, a series of techniques have been developed in order to involve neural architectures in image compression and denoising processes [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Neural Architectures for Correlated Noise Removal in Image Processing

Cocianu

Stan²

2016

Mathematical Problems in Engineering

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The paper proposes a new method that combines the decorrelation and shrinkage techniques to neural network-based approaches for noise removal purposes. The images are represented as sequences of equal sized blocks, each block being distorted by a stationary statistical correlated noise. Some significant amount of the induced noise in the blocks is removed in a preprocessing step, using a decorrelation method combined with a standard shrinkage-based technique. The preprocessing step provides for each initial image a sequence of blocks that are further compressed at a certain rate, each component of the resulting sequence being supplied as inputs to a feed-forward neural architectureFX→FH→FY. The local memories of the neurons of the layersFHandFYare generated through a supervised learning process based on the compressed versions of blocks of the same index value supplied as inputs and the compressed versions of them resulting as the mean of their preprocessed versions. Finally, using the standard decompression technique, the sequence of the decompressed blocks is the cleaned representation of the initial image. The performance of the proposed method is evaluated by a long series of tests, the results being very encouraging as compared to similar developments for noise removal purposes.

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The ANN based detector to remove random-valued impulse noise in images

Cited by 48 publications

References 42 publications

Deep learning for impulsive noise removal in color digital images

Deep learning for impulsive noise removal in color digital images

Real‐time removal of impulse noise from MR images for radiosurgery applications

Neural Architectures for Correlated Noise Removal in Image Processing

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