Abstract

Multi-sampled imaging is a general framework for using pix- els on an image detector to simultaneously sample multiple dimensions of imaging (space, time, spectrum, brightness, polarization, etc.). The mosaic of red, green and blue spectral filters found in most solid-state color cameras is one example of multi-sampled imaging. We briefly de- scribe how multi-sampling can be used to explore other dimensions of imaging. Once such an image is captured, smooth reconstructions along the individual dimensions can be obtained using standard interpolation algorithms. Typically, this results in a substantial reduction of resolution (and hence image quality). One can extract significantly greater resolu- tion in each dimension by noting that the light fields associated with real scenes have enormous redundancies within them, causing difierent dimensions to be highly correlated. Hence, multi-sampled images can be better interpolated using local structural models that are learned off- line from a diverse set of training images. The specific type of structural models we use are based on polynomial functions of measured image in- tensities. They are very effective as well as computationally efficient. We demonstrate the benefits of structural interpolation using three specific applications. These are (a) traditional color imaging with a mosaic of color filters, (b) high dynamic range monochrome imaging using a mo- saic of exposure filters, and (c) high dynamic range color imaging using a mosaic of overlapping color and exposure filters.