###### Abstract
In general, the MRA-based methods can obtain higher spectral resolution, but their spatial resolution is inferior in comparison with the CS-based methods. Moreover, [PERSON] et al. [9] proposed a variational local gradient constraints-based (VLGC) method. This method first calculates the gradient differences between the PAN and MS images in different regions and bands. Then, a local gradient constraint is added to the optimization objective, so as to fully utilize the spatial information contained in the PAN images. [PERSON] et al. [10] proposed a multi-objective decision-based (MOD) method. This method designs a fusion model based on multi-objective decision making, which, in turn, performs a generalized sharpening operation by spectral modulation. [PERSON] et al. [11] proposed a Brovey Transform-based method that integrates Brovey and Laplacian filter to improve the pan-sharpening effects. In recent years, edge-preserving filters (EPF) are extensively applied for image processing. [PERSON] et al. [12] proposed a novel method in accordance with a guided filter. This method applies a guided filter to enhance the spectral resolution of the fused image. EPF can fully utilize the spatial information contained in the original images by preserving the edge information while smoothing it. Thus, EPF is often used as a decomposition method. Another important method is the Pulse Coupled Neural Network (PCNN) model [13]. After several iterations, the PCNN model can accurately extract the features contained in the image. Thus, it is suitable for the field of image fusion [14; 15]. For the traditional PCNN model, the primary obstacle it faces is how to set the free parameters scientifically. Thus, the Parameters Automatic Calculation Pulse Coupled Neural Network (PAC-PCNN) model [16] is introduced into the pan-sharpening process. All parameters in the PAC-PCNN model can be automatically calculated according to the inputs with a fast convergence speed. In general, the fusion images obtained by the CS-based methods can obtain higher spatial resolution, but many spectral information will be lost. The fusion images obtained by the MRA-based method can retain the spectral information, but will lose many spatial details. In particular, the fusion images obtained by the MRA-based methods tend to have spatial distortion, while the fusion images obtained by the CS-based method tend to have spectral distortion. It is important to balance spatial distortion and spectral distortion. Thus, a novel MS and PAN images fusion method based on weighted mean curvature filter (WMCF) decomposition is proposed. The proposed method combines the advantages of EPF decomposition and PAC-PCNN model, and focuses on solving spatial and spectral distortion problems. Firstly, a weighted local spatial frequency-based method (WLSF) is used to fuse all the bands of the MS image to generate intensity component \(IC\). According to an image matting model, \(IC\) is used as the original \(\alpha\) channel for spectral estimation to obtain a foreground and background images. Secondly, a PAN image is decomposed into a small-scale (\(SS\)) image, a large-scale (\(LS\)) image and a basic image by WMCF and GF. The multi-scale morphological detail measure (MSMDM) values are used as the inputs of the PAC-PCNN model. With the PAC-PCNN model guided by the MSMDM values, the basic image and \(IC\) are effectively fused by the PAC-PCNN model. The fused image, and the \(LS\) and \(SS\) images are linearly combined together to construct the last \(\alpha\) channel. Finally, the last \(\alpha\) channel, foreground and background images are reconstructed in accordance with an image matting model to acquire the final fused image. The four primary contributions of this paper are listed below:  
1. An image matting model is introduced to effectively enhance the spectral resolution of the fused image. The preservation of spectral information contained in the MS image is mainly achieved by the image matting model;
2.