Integrating migration velocity analysis (MVA) and full waveform inversion (FWI) can help reduce the high nonlinearity of the classic FWI objective function. The combination of inverting for the long and short wavelength components of the velocity model using a dual objective function that is sensitive to both components is still very expensive and have produced mixed results. We develop an approach that includes both components integrated to complement each other. We specifically utilize the image to generate reflections in our synthetic data only when the velocity model is not capable of producing such reflections. As a result, we get the MVA working when we need it, and mitigate it's influence when the velocity model produces accurate reflections (possible first for the low frequencies). Applications to a layered model, as well as, the Marmousi model demonstrate some of the approach features.