模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Moshi Shibie yu Rengong Zhineng
2014年
11期
977-984
,共8页
脉冲噪声滤波%非噪声像素重构%K-SVD%分层OMP%字典训练
脈遲譟聲濾波%非譟聲像素重構%K-SVD%分層OMP%字典訓練
맥충조성려파%비조성상소중구%K-SVD%분층OMP%자전훈련
Impulse Noise Filter%Non-noisy Pixel Reconstruction%K-SVD%Hierarchical OMP%Dictionary Training
提出一种基于非噪声像素重构的K-SVD( Pixel K-SVD)脉冲噪声滤波方法。在图像重构阶段,以非噪声点像素值为优化目标,利用分层重构改进OMP算法求解优化函数,获得重构图像以提高恢复图像质量；在字典训练阶段,PK-SVD不再固定原子的系数,而是使用重复奇异值分解同时更新原子和系数。将PK-SVD与其他3种方法进行比较,实验结果表明,PK-SVD能得到最稀疏化的字典,较好地抑制脉冲噪声,使得滤波图像较清晰且具有较高的峰值信噪比。
提齣一種基于非譟聲像素重構的K-SVD( Pixel K-SVD)脈遲譟聲濾波方法。在圖像重構階段,以非譟聲點像素值為優化目標,利用分層重構改進OMP算法求解優化函數,穫得重構圖像以提高恢複圖像質量；在字典訓練階段,PK-SVD不再固定原子的繫數,而是使用重複奇異值分解同時更新原子和繫數。將PK-SVD與其他3種方法進行比較,實驗結果錶明,PK-SVD能得到最稀疏化的字典,較好地抑製脈遲譟聲,使得濾波圖像較清晰且具有較高的峰值信譟比。
제출일충기우비조성상소중구적K-SVD( Pixel K-SVD)맥충조성려파방법。재도상중구계단,이비조성점상소치위우화목표,이용분층중구개진OMP산법구해우화함수,획득중구도상이제고회복도상질량；재자전훈련계단,PK-SVD불재고정원자적계수,이시사용중복기이치분해동시경신원자화계수。장PK-SVD여기타3충방법진행비교,실험결과표명,PK-SVD능득도최희소화적자전,교호지억제맥충조성,사득려파도상교청석차구유교고적봉치신조비。
An improved K-SVD method based on non-noisy pixel reconstruction ( PK-SVD) is proposed to filter impulse noise. In the phase of image reconstruction, non-noisy pixels are applied in the construction of optimal function to obtain the reconstructed image and improve the filtering performance, and the optimal function is solved by integrating the hierarchical property into the OMP algorithm. In the phase of dictionary training, PK-SVD uses the iterant K-singular value decomposition to renovate both atoms and their coefficients rather than fixes the coefficients. The simulation results show that compared with the other three methods, PK-SVD obtains the sparsest dictionary and the clearest image with higher peak signal to noise ratio.
