Proc. IEEE International Conference on Image Processing,
Sep. 11-14, 2011,
Patch-based Image Deconvolution Via Joint Modeling Of Sparse Priors
Brian L. Evans
Department of Electrical
and Computer Engineering,
Engineering Science Building,
The University of Texas at Austin,
Austin, TX 78712 USA
The software is available in a rar format, which can be decompressed using
7-zip or other similar software.
Image deconvolution aims to recover an image that has been degraded
by a linear operation such as blurring during image acquisition.
Deconvolution based on maximum a-posteriori (MAP) estimation
requires the global prior probability of the original image.
Conventional methods usually model the image priors by uniformly
characterizing the statistical properties of either some forward
measurements of images or the representation coefficients in frames,
neglecting the local image statistics.
In this paper, we adopt local sparse representation in image
Our contributions include proposing
Experiments indicate that the proposed method can recover the original
image with high peak signal-to-noise ratio (PSNR) and structural
similarity (SSIM) index compared with state-of-the-art methods.
- a joint model of natural images combining sparse representation
of image patches and sparse gradient priors, and
- an efficient iterative algorithm to infer the MAP estimate of
image deconvolution using the proposed model.
Figure 1: Visual comparison of House image in test setting 1.
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Last Updated 09/10/11.