Q 인자의 조절이 가능한 이산 웨이브렛 변환을 이용한 디지털 영상처리
Digital Image Processing Using Tunable Q-factor Discrete Wavelet Transformation
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초록
This paper describes a 2D discrete-time wavelet transform for which the Q-factor is easily specified. Hence, the transform can be tuned according to the oscillatory behavior of the image signal to which it is applied. The tunable Q-factor wavelet transform (TQWT) is a fully-discrete wavelet transform for which the Q-factor, Q, of the underlying wavelet and the asymptotic redundancy (over-sampling rate), r, of the transform are easily and independently specified. In particular, the specified parameters Q and r can be real-valued. Therefore, by tuning Q, the oscillatory behavior of the wavelet can be chosen to match the oscillatory behavior of the signal of interest, so as to enhance the sparsity of a sparse signal representation. The TQWT is well suited to fast algorithms for sparsity-based inverse problems because it is a Parseval frame, easily invertible, and can be efficiently implemented. The TQWT can also be used as an easily-invertible discrete approximation of the continuous wavelet transform. The transform is based on a real valued scaling factor (dilation-factor) and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. The transform is parameterized by its Q-factor and its oversampling rate (redundancy), with modest oversampling rates (e. g. 3-4 times overcomplete) being sufficient for the analysis/synthesis functions to be well localized. Therefore, This method services good performance in image processing fields.
Abstract
This paper describes a 2D discrete-time wavelet transform for which the Q-factor is easily specified. Hence, the transform can be tuned according to the oscillatory behavior of the image signal to which it is applied. The tunable Q-factor wavelet transform (TQWT) is a fully-discrete wavelet transform for which the Q-factor, Q, of the underlying wavelet and the asymptotic redundancy (over-sampling rate), r, of the transform are easily and independently specified. In particular, the specified parameters Q and r can be real-valued. Therefore, by tuning Q, the oscillatory behavior of the wavelet can be chosen to match the oscillatory behavior of the signal of interest, so as to enhance the sparsity of a sparse signal representation. The TQWT is well suited to fast algorithms for sparsity-based inverse problems because it is a Parseval frame, easily invertible, and can be efficiently implemented. The TQWT can also be used as an easily-invertible discrete approximation of the continuous wavelet transform. The transform is based on a real valued scaling factor (dilation-factor) and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. The transform is parameterized by its Q-factor and its oversampling rate (redundancy), with modest oversampling rates (e. g. 3-4 times overcomplete) being sufficient for the analysis/synthesis functions to be well localized. Therefore, This method services good performance in image processing fields.
참고문헌 (8)
- I. W. Selesnick, "Sparse signal representations using the tunable Q-factor wavelet transform", Proceedings of SPIE, Vol. 8138 (Wavelets and Sparsity XIV) (2011)
- 신종홍, "이중 밀도 웨이브렛 변환의 성능 향상을 위한 3방향 분리 처리 기법", (사)디지털산업정보학회 논문지, 8(2), pp.131~143 (2012), (사)디지털산업정보학회[DOI]
- 신종홍, "3중 밀도 이산 웨이브렛 변환을 이용한 디지털 영상처리 기법", (사)디지털산업정보학회 논문지, 8(3), pp.133~145 (2012), (사)디지털산업정보학회
- I. W. Selesnick, "Wavelet transform with tunable Q-factor", IEEE Trans. on Signal Processing, 59(8), pp.3560~3575 (2011)
- Jingyu Yang, "Image Coding Using DualTree Discrete Wavelet Transform", IEEE Trans. on Image Processing, 17(9), pp.1555~1569 (2008)
- I. W. Selesnick, "A higher-density discrete wavelet transform", IEEE Trans. on Signal Processing, 54(8), pp.3039~3048 (2006)
- 임중희, "고밀도 이산 웨이브렛 변환을 이용한 2차원 디지털 영상처리", 한국인터넷방송통신학회 논문지, 13(1), pp.1~8 (2013), 한국인터넷방송통신학회[DOI]
