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Journal of Electronic Imaging

Learning-based superresolution algorithm using quantized pattern and bimodal postprocessing for text images
Author(s): Hui Jung Lee; Dong-Yoon Choi; Byung Cheol Song
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Paper Abstract

This paper proposes a learning-based superresolution algorithm using text characteristics for text images. The proposed algorithm consists of a learning stage and an inference stage. In the learning stage, a sufficient number of low-resolution (LR) to high-resolution (HR) block pairs are first extracted from various LR–HR image pairs that are composed of texts. Then, we classify those block pairs into 512 clusters and, for each cluster, calculate the optimal two-dimensional (2-D) finite impulse response (FIR) filter to synthesize a high-quality HR block from an LR block and store the block-adaptive 2-D FIR filters in a dictionary with their associated index. In the inference stage, we find the best-matched candidate to each input LR block from the dictionary and synthesize the HR block using the optimal 2-D FIR filter. Finally, an HR image is produced via proper postprocessing. Experimental results show that the proposed algorithm provides superior visual quality to images from previous works and outperforms previous processes in terms of computational complexity.

Paper Details

Date Published: 30 November 2015
PDF: 8 pages
J. Electron. Imag. 24(6) 063011 doi: 10.1117/1.JEI.24.6.063011
Published in: Journal of Electronic Imaging Volume 24, Issue 6
Show Author Affiliations
Hui Jung Lee, Inha Univ. (Korea, Republic of)
Samsung Electronics Co., Ltd. (Korea, Republic of)
Dong-Yoon Choi, Inha Univ. (Korea, Republic of)
Byung Cheol Song, Inha Univ. (Korea, Republic of)

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