  Blind pansharpening addresses the problem of generating a high
spatial-resolution multi-spectral (HRMS) image given a low spatial-resolution
multi-spectral (LRMS) image with the guidance of its associated spatially
misaligned high spatial-resolution panchromatic (PAN) image without parametric
side information. In this paper, we propose a fast approach to blind
pansharpening and achieve state-of-the-art image reconstruction quality.
Typical blind pansharpening algorithms are often computationally intensive
since the blur kernel and the target HRMS image are often computed using
iterative solvers and in an alternating fashion. To achieve fast blind
pansharpening, we decouple the solution of the blur kernel and of the HRMS
image. First, we estimate the blur kernel by computing the kernel coefficients
with minimum total generalized variation that blur a downsampled version of the
PAN image to approximate a linear combination of the LRMS image channels. Then,
we estimate each channel of the HRMS image using local Laplacian prior to
regularize the relationship between each HRMS channel and the PAN image.
Solving the HRMS image is accelerated by both parallelizing across the channels
and by fast numerical algorithms for each channel. Due to the fast scheme and
the powerful priors we used on the blur kernel coefficients (total generalized
variation) and on the cross-channel relationship (local Laplacian prior),
numerical experiments demonstrate that our algorithm outperforms
state-of-the-art model-based counterparts in terms of both computational time
and reconstruction quality of the HRMS images.
