Polarization image fusion method with image enhancement

Abstract

Because of their complementary characteristics, intensity images and polarization images are often fused to produce information-rich images. However, the polarization characteristics are easily affected by the object's environment, and the image fusion process may lose important information. In this paper, we propose an unsupervised end-to-end network framework based on a CNN for intensity images and degree of linear polarization images. First, we construct our own polarization dataset to solve the limitations of the training dataset; a hybrid loss function is designed to form an unsupervised learning process; and a Laplace operator enhancement layer is introduced into the network to further improve the quality of the fused images. Subjective and objective comparison experiments prove that the proposed fusion network is visually superior to several classical fusion methods.

Special Discrete Fuzzy Numbers on Countable Sets and Their Applications

“…In this section, we apply the above-investigated special discrete fuzzy numbers on countable sets in the image fusion field [29][30][31]. Firstly, an interpretation of a gray image as a special discrete fuzzy numbers on countable sets is introduced.…”