Infrared and visible image fusion via gradient transfer and total variation minimization

In image fusion, the most desirable information is obtained from multiple images of the same scene and merged to generate a composite image. This resulting new image is more appropriate for human visual perception and further image-processing tasks. Existing methods typically use the same representations and extract the similar characteristics for different source images during the fusion process. However, it may not be appropriate for infrared and visible images, as the thermal radiation in infrared images and the appearance in visible images are manifestations of two different phenomena. To keep the thermal radiation and appearance information simultaneously, in this paper we propose a novel fusion algorithm, named Gradient Transfer Fusion (GTF), based on gradient transfer and total variation (TV) minimization. We formulate the fusion problem as an ℓ1-TV minimization problem, 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. We also generalize the formulation to fuse image pairs without pre-registration, which greatly enhances its applicability as high-precision registration is very challenging for multi-sensor data. The qualitative and quantitative comparisons with eight state-of-the-art methods on publicly available databases demonstrate the advantages of GTF, where our results look like sharpened infrared images with more appearance details.