A Fast Fusion Method for Visible and Infrared Images Using Fourier Transform and Difference Minimization

Abstract

Images of different modalities play important roles in the fields of military, navigation, and target detection, including visible and infrared images. Existing fusion methods can reach relatively good fusion effects, but often make the processing speed slow. To achieve the purpose of faster fusion, this paper proposes a fast fusion of visible and infrared images (FFVI) based on Fourier transform and difference minimization. First, both visible and infrared images are transformed using Fourier transform, and the contour information of infrared image is extracted out at the same time. Then the two transformed images are added together to obtain a composite image, the inverse Fourier transform and the grayscale normalization are performed on the composite transformation image, which will generate an image that reflects the target features. Finally, the resulting image is reconstructed by fusing visible and the generated images according to the principle of "difference minimization". The experimental results show that the novel method FFVI proposed in this paper can attain remarkable fusion effects in terms of the subjective qualitative and the objective quantitative analysis. In addition, this method outperforms several representative image fusion algorithms in the computational speed, which will be beneficial to practical application.

TPFusion: Texture Preserving Fusion of Infrared and Visible Images via Dense Networks

“…We select seven representative IR and VIS image fusion methods to compare with TPFusion, including FPDE [ 5 ], GTF [ 6 ], FFVI [ 7 ], ASR [ 8 ], DenseNetFuse [ 17 ], DenseFuse [ 15 ] and FusionGAN [ 20 ].…”

“…Based on gradient transfer and total variation (TV) minimization, Ma et al formulated the fusion problem as a TV minimization problem [ 6 ], where the data fidelity term keeps the main intensity distribution in the infrared image, and the regularization term preserves the gradient variation in the visible image. Zeng et al proposed a fast fusion of visible and infrared images based on Fourier transform and difference minimization (FFVI) [ 7 ]. In sparse representation methods, Liu et al proposed an adaptive sparse representation model for image denoising and fusion (ASR) [ 8 ].…”

Two-Scale Multimodal Medical Image Fusion Based on Structure Preservation

“…These algorithms have achieved good fusion effects. However, traditional transform domain image fusion algorithms need to perform frequency decomposition and synthesis during fusion, which often have problems, such as fused image distortion and structural information loss of the source images ( Zeng et al, 2020 ). However, in spatial domain, image fusion often needs to block the images during fusion, which often produces serious blocking effects.…”