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

Mutual neighbors and diagonal loading-based sparse locally linear embedding

In this study, a new Locally Linear Embedding (LLE) algorithm is proposed. Common LLE includes three steps. First, neighbors of each data point are determined. Second, each data point is linearly modeled using its neighbors and a similarity graph matrix is constructed. Third, embedded data are extracted using the graph matrix. In this study, for each data point mutual neighborhood conception and loading its covariance matrix diagonally are used to calculate the linear modeling coefficients. Two data points will be named mutual neighbors, if each of them is in the neighborhood of the other. Diagonal loading of the neighboring covariance matrix is applied to avoid its singularity and also to diminish the effect of noise in the reconstruction coefficients. Simulation results demonstrate the performance of applying mutual neighborhood conception and diagonal loading and their combination. Also, the results of applying the mutual neighborhood on Laplacian Eigenmap (LEM) demonstrate the good performance of the proposed neighbor selection method. Our proposed method improves recognition rate on Persian handwritten digits and face image databases