Study on sparse image representations and its application to feature domain image processing

稀疏图像表示及其在特征域图像处理中的应用研究

• 批准号: 17500109
• 负责人: NAKASHIZUKA Makoto
• 金额: $ 1.34万
• 依托单位: Osaka University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2005
• 资助国家: 日本
• 起止时间: 2005 至 2006
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-17500109/
• 关键词: image processing; signal decomposition; basis pursuit; noise removal; wavelet transform; color image processing; image representation; signal modeling; 信号解像度; 特徴抽出; モルフォロジ成分分析; 周期信号

In this study, sparse signal decomposition methods and its application to color images, signal mixtures and periodic signals are proposed. In applications to color image representation, the basis pursuit denoising algorithm is extended to the color image denoising. The basis pursuit denoising predicts the coefficients of a signal from a nosy observation by adding an L1 penalty term on the coefficients. The L1 penalty arises from the assumption that the signal can be decomposed into sparse and statistically independent components. In this study, the L1 penalty is modified to apply the basis pursuit denoising for multichannel signals whose channels are not statistically independent. In experiment, the color image denoising by the basis pursuit by using the modified penalty is demonstrated.For speech and noise separation, we assumed that the speech is stationary within 20-40ms and the duration of the noise is shorter than this period. In our approach, a sparse representation is employed t … More o separate the noise and speech by the difference of its time duration properties. For the sparse representation, a pair of DFT bases that support different time interval were employed to the sparse signal representation. The shorter and the longer DFT bases represent the noise and the speech respectively with a penalty of sparseness. In echoic environments the reverberation of the noises appears in the separated speech signals. In order to suppress the reverberation of the noise, we apply a spectrum subtraction to the separated speech. For the spectrum subtraction, we propose a power estimation method for the noise reverberation. In experiment, we apply the proposed method to noisy speech signals that are corrupted by noise bursts recorded in an echoic environment. We demonstrate that the proposed method can improve about 7-10dB in SNR of the noisy segments.For periodic signal mixtures that are fundamental models of the image mixtures, the sparse periodic decomposition methods that decompose a signal into the small number of periodic signals. The proposed decomposition method imposes a penalty on the resultant periodic subsignals in order to improve the sparsity of decomposition and avoid the overestimation of periods. This penalty is defined as the weighted sum of the $1_2$ norms of the resultant periodic subsignals. This decomposition is approximated by an unconstrained minimization problem. In order to solve this problem, a relaxation algorithm is applied. In the experiments, decomposition results are presented to demonstrate the simultaneous detection of periods and waveforms hidden in signal mixtures. Less

在这项研究中，稀疏信号分解方法及其应用的彩色图像，混合信号和周期信号的建议。在彩色图像表示的应用中，将基追踪去噪算法推广到彩色图像去噪。基追踪去噪通过在系数上添加L1惩罚项来预测来自噪声观测的信号的系数。L1罚分来自于信号可以被分解为稀疏且统计独立的分量的假设。在这项研究中，L1惩罚被修改为适用于多通道信号，其通道不是统计独立的基追踪去噪。在实验中，对基于改进惩罚函数的基追踪算法进行了彩色图像去噪实验，对于语音和噪声的分离，我们假设语音在20- 40 ms内是平稳的，噪声的持续时间小于这个时间段。在我们的方法中，采用稀疏表示， ...更多信息 从而通过噪声和语音的持续时间特性的差异来分离噪声和语音。对于稀疏表示，一对DFT基，支持不同的时间间隔的稀疏信号表示。较短和较长的DFT基分别表示噪声和语音，具有稀疏性的惩罚。在回声环境中，噪声的混响出现在分离的语音信号中。为了抑制噪声的混响，我们对分离后的语音进行谱相减。对于谱减法，我们提出了一种噪声混响的功率估计方法。在实验中，我们将所提出的方法应用到嘈杂的语音信号被破坏的噪声突发记录在回声环境中。实验结果表明，该方法可以使噪声段的信噪比提高约7- 10 dB。对于作为混合图像基本模型的周期混合信号，稀疏周期分解方法将信号分解为少量的周期信号。所提出的分解方法施加的周期子信号的惩罚，以提高分解的稀疏性，避免周期的高估。该惩罚被定义为所得到的周期子信号的$1_2$范数的加权和。这种分解近似于一个无约束的最小化问题。为了解决这个问题，松弛算法。在实验中，分解的结果，展示了同时检测的周期和波形隐藏在信号混合物。少

项目成果

スパース信号表現による音声からの突発性雑音除去

使用稀疏信号表示从语音中突然消除噪声

• 发表时间: 2006
• 期刊: 第19回回路とシステム(軽井沢)ワークショップ講演予講集 1
• 作者: 中静 真;下村 直也;飯國 洋二
• 通讯作者: 飯國 洋二

A short duration noise suppression for speech signals using a sparse signal representation

使用稀疏信号表示的语音信号短时噪声抑制

• 发表时间: 2006
• 期刊: Proceedings on 2006 International Symposium on Nonlinear Theory and its Applications 1
• 作者: 井之浦;辻田;増田;Makoto NAKASHIZUKA
• 通讯作者: Makoto NAKASHIZUKA

A short duration noise suppression method for speech signals by using a sparse signal representation

一种使用稀疏信号表示的语音信号短时噪声抑制方法

• 发表时间: 2006
• 期刊: Proceeding on International Symposium on Nonlinear Theory and its Applications 1
• 作者: K.Tsujita;T.Inoura and T.Masuda;Makoto Nakashizuka
• 通讯作者: Makoto Nakashizuka

A sparse decomposition for periodic signal mixtures

周期性信号混合的稀疏分解

• 发表时间: 2007
• 期刊: Proceedings on 15th International Conference on Digital Signal Processing 1(In press)
• 作者: K.Tsujita;M.Kawakami;K.Tsuchiya;Makoto Nakashizuka
• 通讯作者: Makoto Nakashizuka

エッジ荷重画像強調フィルタの縦続接続型構成

边缘加权图像增强滤波器的级联配置

• 发表时间: 2005
• 期刊: 電子情報通信学会論文誌 J88-A・11
• 作者: 青木 健;中静 真
• 通讯作者: 中静 真