Learning-based superresolution algorithm using quantized pattern and bimodal postprocessing for text images

Hui Jung Lee; Dong-Yoon Choi; Byung Cheol Song

doi:10.1117/1.JEI.24.6.063011

30 November 2015 Learning-based superresolution algorithm using quantized pattern and bimodal postprocessing for text images

Hui Jung Lee, Dong-Yoon Choi, Byung Cheol Song

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 6, 063011 (November 2015). https://doi.org/10.1117/1.JEI.24.6.063011

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.

Citation Download Citation

Hui Jung Lee, Dong-Yoon Choi, and Byung Cheol Song "Learning-based superresolution algorithm using quantized pattern and bimodal postprocessing for text images," Journal of Electronic Imaging 24(6), 063011 (30 November 2015). https://doi.org/10.1117/1.JEI.24.6.063011

Published: 30 November 2015

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