Because hyperspectral imagery is generally low resolution, it is possible for one pixel in the image to contain
several materials. The process of determining the abundance of representative materials in a single pixel is called
spectral unmixing. We discuss the L1 unmixing model and fast computational approaches based on Bregman
iteration. We then use the unmixing information and Total Variation (TV) minimization to produce a higher
resolution hyperspectral image in which each pixel is driven towards a "pure" material. This method produces
images with higher visual quality and can be used to indicate the subpixel location of features.