Digital Images Enhancement with Use of Evolving Neural Networks

Tsoy, Yuri; Spitsyn, V.G.

doi:10.1007/11844297_60

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…In summary, the existing image enhancement methods lack proven human visual perception features since none of the current approaches focus on improving all image qualities [ 27 ]. Multiple scholars have attempted to solve these problems by introducing preprocessing [ 28 – 30 ] and regularizing filters [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

Alenezi

Santosh

2021

International Journal of Biomedical Imaging

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One of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a) by sequencing the activation of a continuous modified HNN (MHNN) based on the geometric correlation of features within various image hyperplanes via pixel gradient vectors and (b) by regulating geometric pixel gradient vectors. These are achieved by regularizing proposed MHNNs under cohomology, which enables them to act as an unconventional filter for pixel spectral sequences. It shifts the focus to both local and global optimizations in order to strengthen feature correlations within each image subspace. As a result, it enhances edges, information content, contrast, and resolution. The proposed algorithm was tested on fifteen different medical images, where evaluations were made based on entropy, visual information fidelity (VIF), weighted peak signal-to-noise ratio (WPSNR), contrast, and homogeneity. Our results confirmed superiority as compared to four existing benchmark enhancement methods.

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Section: Introductionmentioning

confidence: 99%

Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

Alenezi

Santosh

2021

International Journal of Biomedical Imaging

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Post-Processing

Hussein

2011

Computed Radiation Imaging

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Review of subjection between ANN topology and it’s efficiency in solving the problem of improvement image resolution

Кислов

Veremeev

Obukhov

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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The article discusses the influence of ANN topology on its efficiency in solving the problem of increasing the image resolution. An empirical approach is used to establish the fact and determine the nature of the influence. At the beginning of the article, the most commonly used topological techniques for constructing an ANN, which are used to solve the problem of increasing the image resolution, are described. Then, the process of creating an ANN is described based on the above topologies. After that, the learning process of ANN is described. A supervised learning algorithm was used to train the networks, and a set of 7 000 images was used as training data. At the end of the article, an assessment of the efficiency of the trained ANN is carried out, through which the effectiveness of topological solutions is determined. To assess the performance of an artificial neural network, a validation dataset of 100 image is used. Two algorithms are used to assess the quality of enhanced images: SSIM and PSNR. The interpretation of the results obtained is also given.

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Digital Images Enhancement with Use of Evolving Neural Networks

Cited by 3 publications

References 8 publications

Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

Post-Processing

Review of subjection between ANN topology and it’s efficiency in solving the problem of improvement image resolution

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