The spectral filter is one of the crucial tools for signal scale and texture analysis. The main function is to decompose a raw image into a series of multi-scale signatures in the spectral domain. For cross-modal fusion, most of the existing approaches naively employ the decomposition of spectral features to achieve image reconstruction. Unfortunately, it does not sufficiently explore the potential cues of different level components in the spatial domain. In this work, we describe an effective decomposition approach using the spectral filter. Specifically, we aim to build a multi-scale image decomposition framework from the spectral domain to the spatial domain, which is called the multi-scale spatial decomposition approach (MSD). Moreover, building upon spectral total variation (TV), we characterize the spectral signatures into various spatial levels with different filters. A fusion scheme of green fluorescent protein and phase contrast image is introduced, leveraging the multi -scale spatial decomposition approach. The source image is first separated by applying MSD into three spatial bands, namely high-frequency bands, low-frequency bands and structure bands. Our method is simple and effective. The divided bands are obtained by a set of filters and could be processed with the specific fusion strategies to extract the features of the source image. In addition, experiments show that our method produces promising results than other current popular methods.
