Image Resolution Enhancement Using a Hopfield Neural Network

Zhang, Shuangteng; Lu, Yutong

doi:10.1109/itng.2007.105

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…This tendency creates correlation problems, especially when single images are used as inputs, as is the case in the algorithm proposed in this paper. This limitation has surpassed the primary efforts based on HNNs alone [ 43 ] in the areas of image restoration, segmentation, and object classification. Various modifications of HNNs by different researchers have also shown significant limitations in terms of the extraction of the learning vector space and, therefore, have often led to the wrong choice of vector space [ 4 , 44 ].…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%

Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

Alenezi

Santosh

2021

International Journal of Biomedical Imaging

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“…Examples of such networks are Linear Associative Memories (LAM) with single [61] and dual associative learning [192], Hopfield NN [96], [326], Probabilistic NN [130], [304], Integrated Recurrent NN [136], Multi Layer Perceptron (MLP) [196], [354], [385], [547], Feed Forward NN, [232], [233], and Radius Basis Function (RBF) [327], [607].…”

Section: Learning Based Single Image Sr Algorithmsmentioning

confidence: 99%

“…- [189], [190], [213], [298], [299], [322], [323], [338], [339], [349], [433], [453], [469], [480], [510], [537], [133], [150], [151], [179], [160], [162], [174], [207], [209], [210], [215], [216], [217], [223], [231], [237], [241], [247], [251], [252], [273], [281], [285], [345], [308], [309], [317], [323], [326], [331], [344], [349], [353], [36...…”

Section: Assessment Of Sr Algorithmsunclassified

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

589

292

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Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new superresolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

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“…In this paper, a new neural network-based super-resolution technique is presented (partial data of the paper were published in a conference paper 25 ). This technique also takes super-resolution as an inverse problem.…”

Section: Introductionmentioning

confidence: 99%

Image resolution enhancement via image restoration using neural network

Zhang

2011

J. Electron. Imaging

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Image super-resolution aims to obtain a high-quality image at a resolution that is higher than that of the original coarse one. This paper presents a new neural network-based method for image super-resolution. In this technique, the super-resolution is considered as an inverse problem. An observation model that closely follows the physical image acquisition process is established to solve the problem. Based on this model, a cost function is created and minimized by a Hopfield neural network to produce high-resolution images from the corresponding low-resolution ones. Not like some other single frame super-resolution techniques, this technique takes into consideration point spread function blurring as well as additive noise and therefore generates high-resolution images with more preserved or restored image details. Experimental results demonstrate that the high-resolution images obtained by this technique have a very high quality in terms of PSNR and visually look more pleasant. © 2011 SPIE and IS&T.

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Image Resolution Enhancement Using a Hopfield Neural Network

Cited by 6 publications

References 13 publications

Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

Super-resolution: a comprehensive survey

Image resolution enhancement via image restoration using neural network

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