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

Gunnam Suryanarayana (), Vijayakumar Varadarajan (), Siva Ramakrishna Pillutla, Grande Nagajyothi and Ghamya Kotapati
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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

Mathematics, 2022, vol. 10, issue 18, 1-14

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.

Keywords: multiple degradation model; deep networks; image fusion; multi-resolution singular value decomposition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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