Qiang Zhang, (Dept. of Biostatistical Sciences, Wake Forest University, Winston-Salem, NC), V. Pa ul Pauca, (Dept. of Computer Science, Wake Forest University, Winston-Salem, NC), Robert Plemmons, (Dept. of Mathematics and Computer Science, Wake Forest University, Winston-Salem, NC)
Keywords: Phase Diversity, Hyperspectral Images, Multi-Frame Blind Deconvolution, Spectral Unmixing
Abstract:
Our interest here is spectral imaging for space object identification based upon imaging using simultaneous measurements at different wavelengths. AMOS sensors can collect simultaneous images ranging from visible to LWIR. On the other hand, multiframe blind deconvolution (MFBD) has demonstrated success by acquiring near-simultaneous multiple images for reconstructing space objects, and another success has been shown through adding phase diversity (PD) by splitting the light beam in channels with different phase functions. So far, most MFBD and PD applications have been focused on monochromatic images, with a few MFBD studies on multispectral images, also called the wavelength diversity. In particular, B. Calef has shown that wavelength-diverse MFBD is a promising technique for combining data from multiple sensors to yield a higher-quality reconstructed image. Here, we present optimization algorithms to blindly deconvolve observed blurred and noisy hyperspectral images with phase diversity at each wavelength channel. We use the facts that at longer wavelengths, turbulence effects on the phase are less severe, while diffraction effects at shorter wavelengths are less severe. Moreover, because the blurring kernels of all wavelength channels essentially share the same optimal path difference (OPD) function, we have greatly reduced the number of parameters in the blurring kernel. We model the true hyperspectral object by a linear spectral unmixing model, which reduces the number of pixels to be recovered. Because the number of known parameters is far greater than the number of unknowns, the method enjoys an enhanced capability of successful reconstruction. We simultaneously reconstruct the true object, estimate the blurring kernels, and separate the object into spectrally homogeneous segments, each characterized by its support and spectral signature, an important step for analyzing the material compositions of space objects.
Date of Conference: September 10-13, 2013
Track: Adaptive Optics