MSCNN-Multi-Sensor Image Fusion Using Dual channel CNN

Gargi J Trivedi ; Rajesh Sanghavi

Mathematica Applicanda, Tome 51 (2023) no. 2, pp. 165-182.

Résumé

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.

Détail

DOI : 10.14708/ma.v51i2.7204

Classification : 54H30,68U10,94A08
Mots-clés : Convolutional Neural Network (CNN), Discrete wavelet Transformation (DWT), Infrared image (IR), Visible Image (VI), Image fusion