Multiple Degradation Skilled Network for Infrared and Visible Image Fusion Based on Multi-Resolution SVD Updation

Existing infrared (IR)-visible (VIS) image fusion algorithms demand source images with the same resolution levels. However, IR images are always available with poor resolution due to hardware limitations and environmental conditions. In this correspondence, we develop a novel image fusion model that...

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Bibliographic Details
Published in:Mathematics (Basel) 2022-09, Vol.10 (18), p.3389
Main Authors: Suryanarayana, Gunnam, Varadarajan, Vijayakumar, Pillutla, Siva Ramakrishna, Nagajyothi, Grande, Kotapati, Ghamya
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Computer vision
deep networks
Degradation
Image contrast
image fusion
Image processing
Image quality
Image reconstruction
Image resolution
Infrared imagery
Infrared imaging
Mathematical research
Methods
multi-resolution singular value decomposition
multiple degradation model
Neural networks
Quality assessment
Sensors
Singular value decomposition
Wavelet transforms
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Summary:Existing infrared (IR)-visible (VIS) image fusion algorithms demand source images with the same resolution levels. However, IR images are always available with poor resolution due to hardware limitations and environmental conditions. In this correspondence, we develop a novel image fusion model that brings resolution consistency between IR-VIS source images and generates an accurate high-resolution fused image. We train a single deep convolutional neural network model by considering true degradations in real time and reconstruct IR images. The trained multiple degradation skilled network (MDSNet) increases the prominence of objects in fused images from the IR source image. In addition, we adopt multi-resolution singular value decomposition (MRSVD) to capture maximum information from source images and update IR image coefficients with that of VIS images at the finest level. This ensures uniform contrast along with clear textural information in our results. Experiments demonstrate the efficiency of the proposed method over nine state-of-the-art methods using five image quality assessment metrics.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10183389