• Title/Summary/Keyword: Parseval frame

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SHARPENING LOWER BOUND IN SOME INEQUALITIES FOR FRAMES IN HILBERT SPACES

  • Sultanzadeh, Fahimeh;Hassani, Mahmood;Omidvar, Mohsen Erfanian;Gol, Rajab Ali kamyabi
    • Korean Journal of Mathematics
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    • v.29 no.4
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    • pp.725-732
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    • 2021
  • This paper aims to present a new lower bound for some inequalities related to Frames in Hilbert space. Some refinements of the inequalities for general frames and alternate dual frames under suitable conditions are given. These results refine the remarkable results obtained by Balan et al. and Gavruta.

Tunable Q-factor 2-D Discrete Wavelet Transformation Filter Design And Performance Analysis (Q인자 조절 가능 2차원 이산 웨이브렛 변환 필터의 설계와 성능분석)

  • Shin, Jonghong
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.11 no.1
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    • pp.171-182
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    • 2015
  • The general wavelet transform has profitable property in non-stationary signal analysis specially. The tunable Q-factor wavelet transform is a fully-discrete wavelet transform for which the Q-factor Q and the asymptotic redundancy 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 transform is based on a real valued scaling 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 over-sampling rate, with modest over-sampling rates being sufficient for the analysis/synthesis functions to be well localized. This paper describes filter design of 2D discrete-time wavelet transform for which the Q-factor is easily specified. With the advantage of this transform, perfect reconstruction filter design and implementation for performance improvement are focused in this paper. Hence, the 2D transform can be tuned according to the oscillatory behavior of the image signal to which it is applied. Therefore, application for performance improvement in multimedia communication field was evaluated.

Digital Image Processing Using Tunable Q-factor Discrete Wavelet Transformation (Q 인자의 조절이 가능한 이산 웨이브렛 변환을 이용한 디지털 영상처리)

  • Shin, Jong Hong
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.10 no.3
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    • pp.237-247
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    • 2014
  • 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.