We present an approach for imaging the polarization state of scene
points in a wide field of view, while enhancing the radiometric
dynamic range of imaging systems. This is achieved by a simple
modification of image mosaicking, which is a common technique in
remote sensing. In traditional image mosaics, images taken in
varying directions or positions are stitched to obtain a larger
image. Yet, as the camera moves, it senses each scene point
multiple times in overlapping regions of the raw frames. We rigidly attach to the camera a fixed, spatially varying polarization and attenuation filter. This way, the camera motion-induced multiple measurements per scene point are taken under different optical settings. This is in contrast to the redundant measurements of traditional mosaics. Computational algorithms then analyze the data to extract polarization imaging with high dynamic range across the mosaic field of view. We developed a Maximum Likelihood method to automatically register the images, in spite of the challenging spatially varying effects. Then, we use Maximum Likelihood to handle, in a single framework, variable exposures (due to transmittance variations), saturation, and partial polarization filtering. As a by product, these results enable polarization settings of cameras to change while the camera moves, alleviating the need for camera stability. This work demonstrates the modularity of the Generalized Mosaicing approach, which we recently introduced for multispectral image mosaics. The results are useful for the wealth of polarization imaging applications, in addition to mosaicking applications, particularly remote sensing. We demonstrate experimental results obtained using a system we built.