Super-resolution algorithm exploiting multiple dictionaries based on local image structures

作者：Choi Ik Hyun^*; Lim Kyoung Won; Song Byung Cheol

来源：Optical Engineering, 2013, 52(6): 067002.

DOI：10.1117/1.OE.52.6.067002

• example-based superresolution
• learning-based superresolution
• dictionary
• sharpness enhancement
• local structure analysis

We propose an example-based superresolution algorithm that adaptively exploits multiple dictionaries based on local image structures. Noise, irregularities, and blurred textures are noticeable artifacts in the reconstructed image due to a shortage of relevant examples and false exploration in the dictionary. These artifacts are emphasized during successive enhancement. We alleviate the artifacts by constructing multiple dictionaries coupled with different sharpness levels during the learning phase. We exploit these dictionaries adaptively based on local image structures during the synthesis phase. Experimental results show that the proposed algorithm provides more detailed images with significantly reduced artifacts while consuming only 8.6% of storage capacity and 0.25% of CPU running time in comparison with a typical examplebased superresolution algorithm based on neighbor embedding. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI:10.1117/1.OE.52.6.067002]

• 出版日期2013-6