Regularized adaptive super-resolution using kernel estimation-based edge reconnection and kernel orientation constraints | IEEE Conference Publication | IEEE Xplore

Regularized adaptive super-resolution using kernel estimation-based edge reconnection and kernel orientation constraints


Abstract:

This paper presents a spatially adaptive super-resolution (SR) algorithm using homogeneous region analysis for minimizing undesired interpolation artifacts such as aliasi...Show More

Abstract:

This paper presents a spatially adaptive super-resolution (SR) algorithm using homogeneous region analysis for minimizing undesired interpolation artifacts such as aliasing and jagged edges. The proposed regularized SR algorithm incorporates two constraints enforcing (i) disconnected edges to be reconnected and (ii) orientation-adaptive regularization. By combining two constraints in the regularization framework, the proposed SR algorithm can significantly reduce aliasing artifacts and, as a result, produce edge-preserved high-resolution (HR) images. In addition to the formulation of the regularized SR algorithm with hybrid constraints, experimental results show that the proposed SR algorithm improves peak-to-peak signal-to-noise ratio (PSNR) of the reconstructed HR images by up to 5[dB]over existing state-of-the-art SR methods.
Date of Conference: 30 September 2012 - 03 October 2012
Date Added to IEEE Xplore: 21 February 2013
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Conference Location: Orlando, FL, USA

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