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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 17/2021

20.06.2020 | S. I : Hybridization of Neural Computing with Nature Inspired Algorithms

Neural image reconstruction using a heuristic validation mechanism

verfasst von: Dawid Połap, Gautam Srivastava

Erschienen in: Neural Computing and Applications | Ausgabe 17/2021

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Abstract

Image reconstruction is a mathematical process, where the image is compressed into a small representation and derived from this form. The general use of the reconstruction technique finds a place in noise removal from images obtained in medicine or other areas of life. In this paper, we propose a heuristic validation mechanism for training different types of neural networks in the problem of image reconstruction. The main idea is based on finding some important areas on image by heuristic algorithm and train network until a certain level of entropy of these areas is achieved. The mathematical model of this technique is described and supported by experimental results on different datasets with complex analysis of different heuristics. Proposed approach shows that it can reduce the average time of training process using convolutional neural networks.
Vorheriger Artikel Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm
Nächster Artikel Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm

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Metadaten
Titel
Neural image reconstruction using a heuristic validation mechanism
verfasst von
Dawid Połap
Gautam Srivastava
Publikationsdatum
20.06.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 17/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05046-8

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