Multispectral and SAR Image Fusion for Multiscale Decomposition Based on Least Squares Optimization Rolling Guidance Filtering

Abstract

Multispectral and SAR image fusion is one of the key technologies to improve image quality. The fusion method of multi-scale decomposition includes two aspects: the decomposition of image and the design of fusion rule. There are some problems in the traditional decomposition methods, such as gradient reversals, halos and other artifacts, and limited scale separation of space overlapping features. In addition, the quality of fusion images is greatly affected by the fusion rule design. Therefore, a novel method based on least squares optimization rolling guidance filtering for multi-scale decomposition is proposed. All gradients of rolling guidance filtering are optimized by least squares to suppress artifacts such as gradient inversion, and then combined with Gaussian filtering for image decomposition to eliminate interference texture and speckle noise while preserving edge details. At the same time, the decomposition of image is extended to multi-scale space to achieve scale separation of space overlapping features, which is convenient for multi-level fusion of image features. In the end, based on the scale of decomposition, the results fall into three layers, and coupled neural P system and other rules are designed for different layers of information fusion. The results indicate that this method has good visual effect and outperforms all the other comparison methods on nine evaluation indexes.