QRCP Decomposition-Based Hybrid Approach for Fusion of Visible and Infrared Images

Abstract

Image fusion integrates several images from different modalities into a single image which contains more spatial and spectral resolution. Artifacts, smoothing and ringing are major issues in convolutional neural networks, edge-preserving filters and transform-based image fusion methods. Orthogonal rectangular with column pivoting (QRCP) matrix factorization-based hybrid approach is proposed in this work to overcome the above issues and to improve the fusion of visible and infrared images. QRCP decomposition is an accurate matrix decomposition that separates base layer and detail layer from source images. Discrete cosine transform and local spatial frequency concept are employed to fuse the base layers. Weight maps are utilized to transfer information into detail layers that are obtained directly from base layers. The obtained fused image is a linear combination of final base layer and final detail layer. The proposed method outperforms the existing methods in terms of performance measures such as entropy, spatial frequency, mutual information, normalized weight performance index, mean and standard deviation.