Pyramid Image Fusion Based on Contourlet and Enhanced Structural Decomposition

Recently, a method for multi-exposure images fusion based on structural decomposition of images into three parts including signal strength, signal structure and signal mean has been introduced. In this paper, we seek to use this decomposition, for images fusion in other fields, including multimodal medical, multi-focus, and infrared and visible images. To increase the fusion quality, besides the introduction of the proposed weighting factor in the structural decomposition, contourlet transformation and the pyramidal structure have also been used. First, each of the K input images are represented into low frequency and high frequency subbands, by using contourlet transform. Then, all the corresponding subbands (resulting from the same scales and directions) are fused with each other, separately and in an iterative process. In this iterative process, first, a separate pyramid structure (including approximation and detail layers) is created for each of the corresponding K subbands. These layers are obtained by the down-sampling of subbands and structural separation based on the proposed new weighting factor. Then, the fusion is performed in the reverse direction of the pyramidal structure and the fused image of the K corresponding subband is obtained. By repeating this process, the fused image will be obtained for all the corresponding subbands. At the end, the final fused image is obtained by the inverse contourlet transformation on the fused images of the subbands. Several visual and quantitative comparisons, with 7 common methods in this field, have been made. In the visual aspect, the proposed method shows the highest quality.