A new model-based image adjustment for the enhancement of multi-resolution image fusion or pansharpening is proposed. Such image adjustment is needed for most pansharpening methods using panchromatic band and/or intensity image (calculated as a weighted sum of multispectral bands) as an input. Due various reasons, e.g. calibration inaccuracies, usage of different sensors, input images for pansharpening: low resolution multispectral image or more precisely the calculated intensity image and high resolution panchromatic image may differ in values of their physical properties, e.g. radiances or reflectances depending on the processing level. But the same objects/classes in both images should exhibit similar values or more generally similar statistics. Similarity definition will depend on a particular application. For a successful fusion of data from two sensors the energy balance between radiances/reflectances of both sensors should hold. A virtual band is introduced to compensate for total energy disbalance in different sensors. Its estimation consists of several steps: first, weights for individual spectral bands are estimated in a low resolution scale, where both multispectral and panchromatic images (low pass filtered version) are available, then, the estimated virtual band is up-sampled to a high scale and, finally, high resolution panchromatic band is corrected by subtracting virtual band. This corrected panchromatic band is used instead of original panchromatic image in the following pansharpening. It is shown, for example, that the performance quality of component substitution based methods can be increased significantly.

翻译：提议为增强多分辨率图像聚合或整形配置而进行基于新模型的图像调整。 对于使用全色带和/或强度图像(作为多光谱带的加权和总和计算)作为输入的多数整形方法, 需要对图像进行这种调整。 各种原因, 例如校准不准确性、 不同传感器的使用、 用于整形的输入图像: 低分辨率多光谱图像或更精确的计算强度图像和高分辨率全色图像, 其物理属性的值可能不同, 例如, 亮度或根据处理级别反射。 但两种图像中的相同对象/ 类应显示相似值或更一般相似的统计数据 。 相似性定义取决于特定的应用程序 。 对于两个传感器中成功的能量平衡, 两个传感器的光度/反射器之间的能量平衡应该保持。 引入一个虚拟波段可以弥补不同传感器中的全部能量偏差, 其估计包括几个步骤 : 首先, 单个光谱波段的比重或反射镜反射法的反向下, 两个图像的比重值应该以甚高分辨率级的平整。 多光谱级的平段, 将显示高分级的图像的平整。