基于盲压缩感知模型的图像重构方法

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。

Image reconstruction method based on blind compressed sensing model

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 method applied to image reconstruction based on the blind compressed sensing (BCS) model is proposed. The proposed 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 improving the image construction quality. The experimental results show that the proposed method has better robustness against noise and maintains better image texture information than the orthogonal matching pursuit based on wavelet method and the total variation method.