Model-based view at multi-resolution image fusion methods and quality assessment measures

We propose to look at multi-resolution image fusion or pan-sharpening task from a model-based perspective. Explicit definition of all models or assumptions used in the derivation of a fusion method allows us to understand the rationale or properties of existing methods and shows a way for a proper usage or proposal/selection of new methods better satisfying the needs of a particular application. Earlier mentioned property 'better' should be measurable quantitatively, e.g. by means of so-called quality measures. The difficulty of a quality assessment task in multi-resolution image fusion is that a reference image is missing. Existing measures or so-called protocols are still not satisfactory because quite often the rationale or assumptions are not valid or not fulfilled. From a model-based view, it follows naturally that a quality assessment measure can be defined as a combination of error model residuals using common or general models assumed in fusion methods. It is shown that most existing methods based on a spectral transformation or filtering are model-based methods. Unfortunately, it was found out that they are based additionally on a pure pixels assumption. Application of such methods for mixed pixels can lead to wrong fusion results. Model-based view analysis shows which methods respect models assumed and thus can help to select methods which deliver correct or physically justified fusion results.