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1

MSCNN-Multi-Sensor Image Fusion Using Dual channel CNN

Trivedi Gargi J., Sanghavi Rajesh

Mathematica Applicanda

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2023

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Vol. 51, no. 2

165--182

EN

This paper describes an image fusion approach based on CNNs and DWT. According to the suggested method, First Each inputted image is decomposed into approximation coefficients and detail coefficients using DWT. The second step is to maximize the weights using CNN with detailed coefficients. Third, using maximum weight and max pooling, the combined detail images are produced. Fourth, an average pooling of the approximate coefficients is used to determine the final approximation coefficients. Lastly, Inverse DWT is then used to combine the detail and final approximation images to produce the final fused image. Experiments are carried out on four different fusion datasets. Different Quality checking metrics are used to analyze the data, and the results are then contrasted with more recent and usual fusion techniques. The result substantiates that the suggested technique performs better than the existing fusion methods. It is also appropriate for real-time applications due to the proposed method's reasonable computational time and simple yet efficient implementation.

PL

Artykuł dotyczy wielosensorowych konwolucyjnych sieci neuronowych (MS CNN) do fuzji obrazów w oparciu o konwolucyjne sieci neuronowe (CNN) i dyskretną transformację falkową (DWT). Zgodnie z sugerowaną metodą, najpierw każdy wprowadzony obraz jest rozkładany na współczynniki aproksymacji i współczynniki szczegółowości przy użyciu DWT. Drugim krokiem jest maksymalizacja wag za pomocą CNN ze szczegółowymi współczynnikami. W trzecim etapie, przy użyciu maksymalnej wagi i maksymalnego łączenia, tworzone są połączone szczegółowe obrazy. W czwartym etapie stosuje się średnią sumę przybliżonych współczynników w celu określenia ostatecznych współczynników przybliżenia. Na koniec stosuje się odwrotną DWT do łączenia obrazów szczegółowych i końcowych przybliżeń w celu uzyskania ostatecznego połączonego obrazu. Eksperymenty przeprowadzane są na czterech różnych zbiorach danych. Do analizy danych wykorzystuje się różne wskaźniki kontroli jakości, a następnie wyniki porównuje się z nowszymi i typowymi technikami łączenia. Wynik potwierdza, że sugerowana technika działa lepiej niż istniejące metody aglutynacji. Nadaje się również do zastosowań w czasie rzeczywistym ze względu na rozsądny czas obliczeń proponowanej metody oraz prostą, ale efektywną implementację.

2

Plantar pressure image fusion for comfort fusion in diabetes mellitus using an improved fuzzy hidden Markov model

Li Zairan, Wang Dan, Dey Nilanjan, Ashour Amira S., Sherratt R. Simon, Shi Fuqian

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 3

742--752

EN

Diabetes mellitus is a clinical syndrome caused by the interaction of genetic and environmental factors. The change of plantar pressure in diabetic patients is one of the important reasons for the occurrence of diabetic foot. The abnormal increase of plantar pressure is a predictor of the common occurrence of foot ulcers. The feature extraction of plantar pressure distribution will be beneficial to the design and manufacture of diabetic shoes that will be beneficial for early protection of diabetes mellitus patients. In this research, texture-based features of the angular second moment (ASM), moment of inertia (MI), inverse difference monument (IDM), and entropy (E) have been selected and fused by using the updown algorithm. The fused features are normalized to predict comfort plantar pressure imaging dataset using an improved fuzzy hidden Markov model (FHMM). In FHMM, type-I fuzzy set is proposed and fuzzy Baum–Welch algorithm is also applied to estimate the next features. The results are discussed, and by comparing with other back–forward algorithms and different fusion operations in FHMM. Improved HMMs with up–down fusion using type-I fuzzy definition performs high effectiveness in prediction comfort plantar pressure distribution in an image dataset with an accuracy of 82.2% and the research will be applied to the shoe-last personalized customization in the industry.

3

An improved feature based image fusion technique for enhancement of liver lesions

Sreeja P., Hariharan S.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

611--623

EN

This paper describes two methods for enhancement of edge and texture of medical images. In the first method optimal kernel size of range filter suitable for enhancement of liver and lesions is deduced. The results have been compared with conventional edge detection algorithms. In the second method the feasibility of feature based pixel wise image fusion for enhancing abdominal images is investigated. Among the different algorithms developed in the medical image fusion pixel level fusion is capable of retaining the maximum relevant information with better implementation and computational efficiency. Conventional image fusion includes multi-modal fusion and multi-resolution fusion. The present work attempts to fuse together, texture enhanced and edge enhanced images of the input image in order to obtain significant enhancement in the output image. The algorithm is tested in low contrast medical images. The result shows an improvement in contrast and sharpness of output image which will provide a basis for a better visual interpretation leading to more accurate diagnosis. Qualitative and quantitative performance evaluation is done by calculating information entropy, MSE, PSNR, SSIM and Tenengrad values.