信号处理
信號處理
신호처리
SIGNAL PROCESSING
2015年
7期
823-832
,共10页
图像复原%Cosparse 解析模型%平移不变小波变换%交替方向乘子法
圖像複原%Cosparse 解析模型%平移不變小波變換%交替方嚮乘子法
도상복원%Cosparse 해석모형%평이불변소파변환%교체방향승자법
image restoration%Cosparse analysis model%translation invariant wavelet transform%alternating direction method of multipliers
基于稀疏表示的图像复原算法大都只利用了图像整体稀疏性和局部稀疏性中的一种,未充分利用图像的先验知识,基于此,本文在稀疏表示框架下,同时引入 Cosparse 解析模型及平移不变小波变换两种稀疏模型,前者对每个图像块进行稀疏表示,后者对整幅图像进行稀疏表示,从而提出一种新的图像复原算法。该算法将图像复原问题归结为双稀疏正则化问题。为求解复杂的双稀疏优化问题,本文运用交替方向乘子法(ADMM,Al-ternating Direction Method of Multipliers)算法将该约束优化问题分解为若干子问题,通过交替迭代求解获得复原图像。实验中对不同类型的模糊图像进行了复原,其结果表明该算法对于各类模糊图像的复原比现有复原算法效果更好,从而验证了算法的有效性。
基于稀疏錶示的圖像複原算法大都隻利用瞭圖像整體稀疏性和跼部稀疏性中的一種,未充分利用圖像的先驗知識,基于此,本文在稀疏錶示框架下,同時引入 Cosparse 解析模型及平移不變小波變換兩種稀疏模型,前者對每箇圖像塊進行稀疏錶示,後者對整幅圖像進行稀疏錶示,從而提齣一種新的圖像複原算法。該算法將圖像複原問題歸結為雙稀疏正則化問題。為求解複雜的雙稀疏優化問題,本文運用交替方嚮乘子法(ADMM,Al-ternating Direction Method of Multipliers)算法將該約束優化問題分解為若榦子問題,通過交替迭代求解穫得複原圖像。實驗中對不同類型的模糊圖像進行瞭複原,其結果錶明該算法對于各類模糊圖像的複原比現有複原算法效果更好,從而驗證瞭算法的有效性。
기우희소표시적도상복원산법대도지이용료도상정체희소성화국부희소성중적일충,미충분이용도상적선험지식,기우차,본문재희소표시광가하,동시인입 Cosparse 해석모형급평이불변소파변환량충희소모형,전자대매개도상괴진행희소표시,후자대정폭도상진행희소표시,종이제출일충신적도상복원산법。해산법장도상복원문제귀결위쌍희소정칙화문제。위구해복잡적쌍희소우화문제,본문운용교체방향승자법(ADMM,Al-ternating Direction Method of Multipliers)산법장해약속우화문제분해위약간자문제,통과교체질대구해획득복원도상。실험중대불동류형적모호도상진행료복원,기결과표명해산법대우각류모호도상적복원비현유복원산법효과경호,종이험증료산법적유효성。
Image restoration algorithms based on sparse representation generally use the whole sparsity or the local sparsity of the image,while not make full use of prior knowledge of the image.Based on this,in the framework of sparse represen-tation,this article proposed a new image restoration algorithm introducing both the Cosparse analysis model leading to a sparse representation of each image patch and translation invariant wavelet transform leading to a sparse representation over the whole image.In the algorithm,the problem of image restoration is expressed as the double sparse regularization prob-lem.To solve the complex double sparse optimization problem,the alternating direction method of multipliers is introduced to decompose the issue into equivalent sub-problems.By alternatively and iteratively solving the sub-problems,the restored image is obtained.In the experiments,the images blurred by different type of blur are restored.The experimental results show that,the proposed algorithm outperforms the existing restoration algorithms.Thus,the effectiveness of the proposed algorithm is verified.