资 源 简 介
Blind image deconvolution is an ill-posed problem that
requires regularization to solve. However, many common
forms of image prior used in this setting have a major draw-
back in that the minimum of the resulting cost function does
not correspond to the true sharp solution. Accordingly, a
range of additional methods are needed to yield good re-
sults (Bayesian methods, adaptive cost functions, alpha-
matte extraction and edge localization). In this paper we
introduce a new type of image regularization which gives
lowest cost for the true sharp image. This allows a very
simple cost formulation to be used for the blind deconvolu-
tion model, obviating the need for additional methods. Due
to its simplicity the algorithm is fast and very robust. We
demonstrate our method on real images with both spatially
invariant and spatially varying blur.
文 件 列 表
blinddeconv
1.png
Blind Deconvolution.pdf
center_kernel_separate.m
deblur.jpg
fast_deconv_bregman.m
fishes.jpg
lyndsey.tif
ms_blind_deconv.asv
ms_blind_deconv.m
mukta.jpg
pcg_kernel_core_irls_conv.m
pcg_kernel_irls_conv.m
pietro.tif
README.txt
solve_image_bregman.asv
solve_image_bregman.m
ss_blind_deconv.m
test_blind_deconv.asv
test_blind_deconv.m
