Fusion of visible and infrared image via compressive sensing using convolutional sparse representation

Abstract

An effective visible light and infrared image fusion method using a deep learning framework is designed to obtain a fused image which contains all the features from infrared and visible images. First, the source images are decomposed into low frequency and high frequency sub bands using wavelet transform. Then the low frequency is fused by maximum fusion rule. For the high frequency sub bands a deep learning network is used to find activity level measurements and then fused using the maximum fusion rule. For reconstruction, the optimized orthogonal matching pursuit algorithm and inverse wavelet transform are used.

Keywords

• Sparsity
• Compressive sensing
• Convolutional sparse representation
• Image fusion
• Optimized orthogonal matching pursuit (OOMP)