Unified image fusion framework with learning-based 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 framework, an important area is selected using the importance measure obtained for each image type in each application. The key is to learn this application-adaptive importance measure that can select the important area irrespective of the input image type without manually designing the algorithm for each application. Then, the fused intensity is generated using Poisson image reconstruction. Experimental results demonstrate that the proposed framework is effective for various applications including depth-perceptible image enhancement, temperature-preserving image fusion, and haze removal.