CAJ | 학술논문

标准的图像压缩感知算法未利用像素间的邻域结构信息和图像子块的自相似性。针对这一问题，本文将图像分成重叠的图像子块，用冗余字典自适应地稀疏表示图像，同时将用自回归模型表示的图像局部相关性和非局部相似性作为先验知识运用到压缩感知图像重构中，提出了结合图像的局部相关性和非局部相似性的多尺度分块压缩感知方法。实验结果表明，本文算法可以有效提高图像重构的视觉效果和峰值信噪比。
표준적도상압축감지산법미이용상소간적린역결구신식화도상자괴적자상사성。침대저일문제，본문장도상분성중첩적도상자괴，용용여자전자괄응지희소표시도상，동시장용자회귀모형표시적도상국부상관성화비국부상사성작위선험지식운용도압축감지도상중구중，제출료결합도상적국부상관성화비국부상사성적다척도분괴압축감지방법。실험결과표명，본문산법가이유효제고도상중구적시각효과화봉치신조비。
The standard image compressed sensing ignores the neighborhood structure information of the image pixels and the simi-larity of image patches. To address this issue, the whole image was cut into overlapping patches and the image patches are repres-ented by adaptive redundant dictionary. The image local correlation which is represented by autoregressive model and image non-local similarity was used in image compressed sensing as prior. And then a new multiscale block compressed sensing algorithm by combing image local correlation and nonlocal similarity is proposed in this paper. Simulation results show that the performance of the proposed algorithm has significant performance improvement in visual quality of the reconstructed image and peak signal-to-noise ratio.