论文标题
红外线和可见图像的贝叶斯融合
Bayesian Fusion for Infrared and Visible Images
论文作者
论文摘要
红外和可见的图像融合一直是图像融合中的热门问题。在此任务中，有望获得融合图像，其中包含可见图像的梯度和详细纹理信息以及热辐射以及突出显示红外图像的目标。在本文中，为红外和可见图像建立了一种新颖的贝叶斯融合模型。在我们的模型中，图像融合任务被抛入回归问题。为了衡量可变的不确定性，我们以分层的贝叶斯方式制定模型。为了使融合图像满足人类视觉系统的满足，该模型结合了总变化（TV）惩罚。随后，通过预期最大化（EM）算法有效地推断该模型。我们使用几种最新方法测试了TNO和NIR图像融合数据集的算法。与以前的方法相比，新型模型可以生成具有高光目标和丰富纹理细节的更好的融合图像，从而可以提高目标自动检测和识别系统的可靠性。
Infrared and visible image fusion has been a hot issue in image fusion. In this task, a fused image containing both the gradient and detailed texture information of visible images as well as the thermal radiation and highlighting targets of infrared images is expected to be obtained. In this paper, a novel Bayesian fusion model is established for infrared and visible images. In our model, the image fusion task is cast into a regression problem. To measure the variable uncertainty, we formulate the model in a hierarchical Bayesian manner. Aiming at making the fused image satisfy human visual system, the model incorporates the total-variation(TV) penalty. Subsequently, the model is efficiently inferred by the expectation-maximization(EM) algorithm. We test our algorithm on TNO and NIR image fusion datasets with several state-of-the-art approaches. Compared with the previous methods, the novel model can generate better fused images with high-light targets and rich texture details, which can improve the reliability of the target automatic detection and recognition system.
