CAJ | 학술논문

信号分解的稀疏程度决定了压缩感知重构信号的精度，针对标准正交基稀疏程度的不足，提出了基于混合字典的压缩感知图像分解和重构方法。构建匹配图像边缘和纹理的二维Gabor字典，将图像在离散余弦字典与建立的二维Gabor字典上进行混合稀疏分解，得到图像的光滑成分、边缘成分和纹理成分。对得到的稀疏成分进行CS观测，通过求解一个优化问题重构图像。实验结果表明，构造的混合字典能够对图像进行更加稀疏的表示，在相同的采样率下，图像的重构质量优于标准正交基分解。
신호분해적희소정도결정료압축감지중구신호적정도，침대표준정교기희소정도적불족，제출료기우혼합자전적압축감지도상분해화중구방법。구건필배도상변연화문리적이유Gabor자전，장도상재리산여현자전여건립적이유Gabor자전상진행혼합희소분해，득도도상적광활성분、변연성분화문리성분。대득도적희소성분진행CS관측，통과구해일개우화문제중구도상。실험결과표명，구조적혼합자전능구대도상진행경가희소적표시，재상동적채양솔하，도상적중구질량우우표준정교기분해。
The precision of the signal reconstruction by compressed sensing is decided by the sparse degree of signal decomposi-tion. According to the insufficient sparse degree of standard orthogonal basis, this paper establishes two-dimensional Gabor dic-tionaries which can match the edge and texture of an image. Mixed sparse decomposing of the image on the discrete cosine dic-tionary and established two-dimensional Gabor dictionaries, it can get the smooth component, edge component and texture com-ponent of the image. It measures the obtained sparse components by compressed sensing, by solving an optimization problem, original image can be reconstructed. Experimental results demonstrate that the multi-component dictionaries can give more sparse representation for an image, and the quality of image reconstruction is better than standard orthogonal basis sparse decom-position in the same sampling rate.