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

Author

Listed:
  • Gunnam Suryanarayana

    (Department of Electronics and Communications, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada 520007, India)

  • Vijayakumar Varadarajan

    (Department of Computer Science Engineering, The University of New South Wales, Sydney, NSW 2052, Australia)

  • Siva Ramakrishna Pillutla

    (School of Electronics Engineering, VIT-AP University, Amaravati 522237, India)

  • Grande Nagajyothi

    (Department of Electronics and Communications, Golden Valley Integrated Campus, Madanapalli 517325, India)

  • Ghamya Kotapati

    (Department of Computer Science Engineering, Sree Vidyanikethan Engineering College, Tirupati 517102, India)

Abstract

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.

Suggested Citation

  • Gunnam Suryanarayana & Vijayakumar Varadarajan & Siva Ramakrishna Pillutla & Grande Nagajyothi & Ghamya Kotapati, 2022. "Multiple Degradation Skilled Network for Infrared and Visible Image Fusion Based on Multi-Resolution SVD Updation," Mathematics, MDPI, vol. 10(18), pages 1-14, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3389-:d:918336
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    References listed on IDEAS

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    1. Xing Zhang & Chongchong Zhang & Zhuoqun Wei, 2019. "Carbon Price Forecasting Based on Multi-Resolution Singular Value Decomposition and Extreme Learning Machine Optimized by the Moth–Flame Optimization Algorithm Considering Energy and Economic Factors," Energies, MDPI, vol. 12(22), pages 1-23, November.
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    Cited by:

    1. Nikita Andriyanov, 2022. "Application of Graph Structures in Computer Vision Tasks," Mathematics, MDPI, vol. 10(21), pages 1-14, October.

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