Unified image fusion based on application-adaptive importance measure

Takashi Shibata, Masayuki Tanaka, Masatoshi Okutomi

Abstract: This paper presents a novel unified image fusion framework based on an application-adaptive importance measure. In the proposed method, an important area is selected pixel-by-pixel using the importance measure which is designed for each image type in each application. Then, the fused intensity is generated by a Poisson image editing. The main contribution is to provide a generalized image fusion framework enables us to deal with various different types of images for many applications. Experimental results show that the proposed method is effective for various applications including depth-perceptible image enhancement, temperature-preserving image fusion, optical flow fusion, and haze removal.