Proceedings of the Korean Society of Broadcast Engineers Conference (한국방송∙미디어공학회:학술대회논문집)
- 2009.01a
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- Pages.254-257
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- 2009
LEARNING-BASED SUPER-RESOLUTION USING A MULTI-RESOLUTION WAVELET APPROACH
- Kim, Chang-Hyun (School of Electrical Engineering and Computer Science, KAIST) ;
- Choi, Kyu-Ha (School of Electrical Engineering and Computer Science, KAIST) ;
- Hwang, Kyu-Young (SAIT, Samsung Electronics Inc.) ;
- Ra, Jong-Beom (School of Electrical Engineering and Computer Science, KAIST)
- Published : 2009.01.12
Abstract
In this paper, we propose a learning-based super-resolution algorithm. In the proposed algorithm, a multi-resolution wavelet approach is adopted to perform the synthesis of local high-frequency features. To obtain a high-resolution image, wavelet coefficients of two dominant LH- and HL-bands are estimated based on wavelet frames. In order to prepare more efficient training sets, the proposed algorithm utilizes the LH-band and transposed HL-band. The training sets are then used for the estimation of wavelet coefficients for both LH- and HL-bands. Using the estimated high frequency bands, a high resolution image is reconstructed via the wavelet transform. Experimental results demonstrate that the proposed scheme can synthesize high-quality images.