A new patch-based two-scale decomposition for infrared and visible image fusion

Abstract

Typically, in existing two-scale fusion method, source images are first processed by a filter to obtain two layers, including a base layer and a detail layer. Second, a specific fusion strategy is applied to fuse these layers. Herein, the fusion of the base layer influences the contrast and clearness of final fusion result because the base layer may contain large scale variations in intensity. However, due to the inherent limitation of the base layer based on the filtering decomposition, the contrast of fusion result is easily reduced, or even the detail of the source images, in the fusion result, may be blurred. In this paper, a new two-scale fusion approach is proposed. In the process of two-scale decomposition, a new patch-based low-rank representation is proposed to obtain the two layers of the source images. In the proposed decomposition approach, the expression of the base layer is converted from a single filtering decomposition to a transform domain. It consists of a dictionary and corresponding coefficients. In the process of fusion, we design a global statistical fusion rule based on the coefficient value to achieve the base layer fusion, which improves the contrast of the final fusion result. For detail layer fusion, the L1-norm strategy is chosen to integrate small scale features contained in the detail layer into the final fusion result, which can contain clear detail. Experimental results illustrate that the proposed framework outperforms traditional approaches on the public database.