###### Abstract  
Since the hardware limitations of satellite sensors, the spatial resolution of multispectral (MS) images is still not consistent with the panchromatic (PAN) images. It is especially important to obtain the MS images with high spatial resolution in the field of remote sensing image fusion. In order to obtain the MS images with high spatial and spectral resolutions, a novel MS and PAN images fusion method based on weighted mean curvature filter (WMCF) decomposition is proposed in this paper. Firstly, a weighted local spatial frequency-based (WLSF) fusion method is utilized to fuse all the bands of a MS image to generate an intensity component _IC_. In accordance with an image matting model, _IC_ is taken as the original \(x\) channel for spectral estimation to obtain a foreground and background images. Secondly, a PAN image is decomposed into a small-scale (_SS_), large-scale (_LS_) and basic images by weighted mean curvature filter (WMCF) and Gaussian filter (GF). The multi-scale morphological detail measure (MSMDM) value is used as the inputs of the Parameters Automatic Calculation Pulse Coupled Neural Network (PAC-PCNN) model. With the MSMDM-guided PAC-PCNN model, the basic image and _IC_ are effectively fused. The fused image as well as the _LS_ and _SS_ images are linearly combined together to construct the last \(x\) channel. Finally, in accordance with an image matting model, a foreground image, a background image and the last \(x\) channel are reconstructed to acquire the final fused image. The experimental results on four image pairs show that the proposed method achieves superior results in terms of subjective and objective evaluations. In particular, the proposed method can fuse MS and PAN images with different spatial and spectral resolutions in a higher operational efficiency, which is an effective means to obtain higher spatial and spectral resolution images. weighted mean curvature filter (WMCF); image matting model; multi-scale morphological measure (MSMDM); parameters automatic calculation pulse coupled neural network (PAC-PCNN) Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], J.A. A Multispectral and Panchromatic Images Fusion Method Based on Weighted Mean Curvature Filter Decomposition. _Appl. Sci._**2022**, _12_, 8767. [[https://doi.org/10.3390/app1278767](https://doi.org/10.3390/app1278767)]([https://doi.org/10.3390/app1278767](https://doi.org/10.3390/app1278767))  
Academic Editor: [PERSON]  
Received: 12 July 2022  
Accepted: 30 August 2022  
Submitted: 31 August 2022  
Submitted: 30 August 2022  
transform-based method [1], principal component analysis-based (PCA) method [2], adaptive Gram-Schmidt-based (AGS) method [3], etc. Moreover, [PERSON] et al. [4] proposed a partial replacement-based adaptive component substitution-based (PRACS) method. This method generates high-resolution fused components by partial substitution and performs high-frequency injection based on statistical ratios. [PERSON] et al. [5] proposed a band-dependent spatial-detail with physical constrains-based (BDSD-PC) method. This method adds physical constraints to the optimization problem for guiding the band-dependent spatial-detail method toward a more robust solution. In general, the CS-based methods can effectively enhance the spatial resolution of the fused image. On the other hand, it may cause the spectral distortion to some extent. The multi-resolution analysis-based (MRA) methods mainly include the Wavelet Transform-based (WT) [6], Curvelet Transform-based [7], and Non-Subsampled Shearlet Transform-based (NSST) methods [8]. With the MRA-based methods, the source image is disintegrated into several sub-images with different scales through some multi-scale decomposition methods. Then, different fusion rules are developed for sub-images at different scales, so that the sub-images can be fused. Finally, the final fused image is obtained by inverse transformation.