Hyper-spectral content aware resizing

Abstract

Image resizing is performed for many reasons in image processing. Often, it is done to reduce or enlarge an image for display. It is also done to reduce the bandwidth needed to transmit an image. Most image resizing algorithms work based on principles of spatial or spatial frequency interpolation. One drawback to these algorithms is that they are not image content aware and can fail to preserve relevant features in an image, especially during size reduction. Recently, a content aware image resizing algorithm, called seam carving, was developed. In this paper we discuss an extension of the seam carving algorithm to hyper-spectral imagery. For a hyper-spectral image with an MxN field of view and with P spectral layers, our algorithm identifies a one pixel wide path through the image field of view containing a minimum of information and then removes it. This process is repeated until the image size is reduced to the desired dimension. Information content is assessed using normalized spatial power metrics. Several such metrics have been tested with varying results. The resulting carved hyper-spectral image has the minimum reduction in information for the resizing based upon energy metrics used to quantify information. We will present the results of seam carving applied to imagery sets of: three spectra RGB imagery from a standard still camera, two spectra imagery generated synthetically, and three spectra imagery captured with VNIR, SWIR, and LWIR cameras.