Image Fusion Based on the \({\Delta ^{ - 1}} - T{V_0}\) Energy Function

Entropy

Chao Ma¹  Qiwei Xie²  Seiichi Mita³  Vijay John³  Qian Long³  Chunzhao Guo⁴ 

[1] Department of General Education, Macau University of Science and Technology, Macau, China;Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;Research Center for Smart Vehicles, Toyota Technological Institute, Nagoya, Aichi 468-8511, Japan;Toyota Central R&D Labs, Yokomichi, Aichi 480-1192, Japan;

关键词: image fusion;    inverse transform of the Laplace operator (\(\Delta^{-1}\) );    sparse norm;    total variation;   

DOI  :  10.3390/e16116099

来源: DOAJ

【 摘 要 】

This article proposes a \({\Delta^{-1}}-T{V_0}\) energy function to fuse a multi-spectral image with a panchromatic image. The proposed energy function consists of two components, a \(TV_0\) component and a \(\Delta^{-1}\) component. The \(TV_0\) term uses the sparse priority to increase the detailed spatial information; while the \({\Delta ^{ - 1}}\) term removes the block effect of the multi-spectral image. Furthermore, as the proposed energy function is non-convex, we also adopt an alternative minimization algorithm and the \(L_0\) gradient minimization to solve it. Experimental results demonstrate the improved performance of the proposed method over existing methods.

【 授权许可】

Unknown