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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Singh, A. Ahuja, N. |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA (Singh, A.; Ahuja, N.) |
| Abstract | In this paper we propose a new image domain prior term for regularizing the super-resolution reconstruction algorithm. This term encourages preserving the local ramp structure around edges, in the reconstruction algorithm. Ramp at a pixel is defined as the steepest sequence of monotonically increasing (or decreasing) pixels among all feasible directions around the pixel. As described in previous work, ramp based modeling is a richer characterization of local image structure than conventional gradients. Our proposed ramp-preserving constraint image is obtained by first running an accurate segmentation algorithm (which is itself obtained by ramp based modeling) on the low resolution image. We then perform a domain transformation of the pixels belonging to the steepest ramps at the edge pixels, in order to preserve sharpness. The resulting non-uniformly spaced image is then upscaled to a uniform, high resolution grid, using an edge preserving non-uniform interpolation scheme. This image is then used both as the prior constraint as well as the initial guess for the iterative super-resolution reconstruction algorithm. Our results compare favorably to the classical back-projection algorithm as well as newer methods which use learning based gradient domain priors. |
| Starting Page | 947 |
| Ending Page | 951 |
| File Size | 9950908 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781479923410 |
| DOI | 10.1109/ICIP.2013.6738196 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-09-15 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image edge detection Image resolution Interpolation Image segmentation Vectors Image reconstruction Kernel regularization Super-resolution image prior segmentation |
| Content Type | Text |
| Resource Type | Article |
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