Pansharpening With Multiscale Normalized Nonlocal Means Filter: A Two-Step Approach

Pansharpening aims to synthesize a high-spatial-resolution multispectral (MS) image by fusing a panchromatic (PAN) image and a low-resolution MS image. The multiresolution analysis (MRA)-based methods are a popular group of pansharpening methods. However, in the MRA-based methods, spatial distortions may occur in the pansharpened product due to the misalignment of PAN and MS data. To address the spatial distortion issue in MRA-based methods, this paper proposes a two-step approach, which consists of the coarse step and the refined step. The coarse step produces a preliminary result using the traditional details injection model. Then, the preliminary product is refined with a second details injection operation in the refined step. Moreover, in our proposed two-step approach, a novel multiscale decomposition based on a normalized nonlocal means (NNLM) filter is developed to extract the spatial detail. Compared with the original nonlocal means filter, the designed NNLM makes the similarity measure more robust and accurate by exploiting the normalized intensity value and the mean value jointly. The experimental results on various satellite data demonstrate the superiority of the proposed pansharpening scheme by comparing with ten well-known methods.