Wavefusion: Wavelet Assistant Fusion Model for Pan-Sharpening

Pan-sharpening refers to obtain a high-resolution multispectral (HRMS) image by fusing a panchromatic (PAN) image and a low-resolution multispectral (LRMS) image. Recently, convolutional neural networks (CNNs) have achieved great success in pan-sharpening. However, the down-sampling operations in commonly used CNN-based models lead to information loss, and the corresponding up-sampling operations usually introduce some undesirable artifacts, resulting in suboptimal fusion results. In this paper, we propose a simple but effective wavelet assistant fusion model (WaveFusion) to address aforementioned issue. The proposed model consists of three parts, namely a wavelet feature extraction (WFE) part, a wavelet feature fusion (WFF) part and a reconstruction part. With the assistance of the wavelet transform and also a simple alignment operation, WaveFusion obtains the best fusion result compared with some state-of-the-art methods, especially for the fusion at the full resolution.