CAJ | 학술논문

为了解决压缩感知重建中噪声引起图像质量明显下降的问题，研究了自适应学习的压缩感知模型，提出了一种盲压缩感知图像重构方法。该方法采用盲压缩感知的稀疏矩阵与稀疏基交替更新的思想，应用了图像冗余变换和初始组合余弦变换基相结合的迭代策略，解决了压缩感知中的稀疏基难于表示的问题，抑制了噪声，提高了图像重构质量。通过实验验证所提方法较基于小波变换的正交匹配追踪方法和全变差方法有明显的噪声抑制功能，且能保持较好的图像纹理信息。
위료해결압축감지중건중조성인기도상질량명현하강적문제，연구료자괄응학습적압축감지모형，제출료일충맹압축감지도상중구방법。해방법채용맹압축감지적희소구진여희소기교체경신적사상，응용료도상용여변환화초시조합여현변환기상결합적질대책략，해결료압축감지중적희소기난우표시적문제，억제료조성，제고료도상중구질량。통과실험험증소제방법교기우소파변환적정교필배추종방법화전변차방법유명현적조성억제공능，차능보지교호적도상문리신식。
In order to solve the problem of decreased image quality due to the poor robustness against noise in compressed sensing (CS)reconstruction,the CS model based on adaptive learning is studied and a new me-thod applied to image reconstruction based on the blind compressed sensing (BCS)model is proposed.The pro-posed method employs the idea of alternating update of sparse base and sparse matrix from the BCS model,and adopts the strategy which combines the image redundant transform and initial combination of discrete cosine transform base,thus solving the problem that the sparse base is hard to express,restraining the noise and im-proving the image construction quality.The experimental results show that the proposed method has better ro-bustness against noise and maintains better image texture information than the orthogonal matching pursuit based on wavelet method and the total variation method.