With the development of remote sensing data acquisition technique, more and more image data have been acquired. To process the explodely increasing data, data fusion technique has been used widely. Compared with traditional methods such as IHS, PCA, HPF, etc, multiresolution analysis data fusion methods can preserve spectral character much better. There've been many multiresolution-based data fusion methods so far, moreover, wavelet-based and pyramid-based methods have played important roles in data fusion area. However, traditional wavelet-based fusion methods always decompose every multisource or multitemporal image into several parts as low frequency and high frequency, and then continue the decomposition to the low frequency part. Afterwards, fuse the same levels low and high frequency parts of different images, and then reconstruct the fused image. Now a novel wavelet transform-based fusion method is described, which uses a wavelet package to decompose the multisource or multitemporal images at either low or high frequency parts. Then, at the same level, utilizes a threshold and weight algorithm to fuse the corresponding low frequency parts, at the same time applies Lis high-pass filter on fusing the high frequency parts. At last, experiment is performed on two multitemporal images to validate this method.