CAJ | 학술논문

由于单正则化图像复原算法所利用的先验信息有限，影响了复原图像的质量。为克服此类算法的不足，融入更多的先验信息，改善图像复原的效果。在稀疏表示的理论框架下，提出了一种多正则优化图像复原算法。该算法将图像复原表示为含多正则项的全局优化问题，为有效处理这一复杂的图像复原问题，采用交替优化策略并借助变量分裂将其分解为若干优化子问题。其中，uj+1子问题可微，可直接得到其解析解。不可微的wj+1和vj+1子问题，则通过邻近映射求解。实验过程中对三种不同类型的退化图像进行了复原，所得结果验证了该算法的有效性。与FISTA（Fast Iterative Shrinkage-Thresholding Algorithm）和Split Bregman等单正则化图像复原算法相比，所提算法的复原效果和时间性能更优。
유우단정칙화도상복원산법소이용적선험신식유한，영향료복원도상적질량。위극복차류산법적불족，융입경다적선험신식，개선도상복원적효과。재희소표시적이론광가하，제출료일충다정칙우화도상복원산법。해산법장도상복원표시위함다정칙항적전국우화문제，위유효처리저일복잡적도상복원문제，채용교체우화책략병차조변량분렬장기분해위약간우화자문제。기중，uj+1자문제가미，가직접득도기해석해。불가미적wj+1화vj+1자문제，칙통과린근영사구해。실험과정중대삼충불동류형적퇴화도상진행료복원，소득결과험증료해산법적유효성。여FISTA（Fast Iterative Shrinkage-Thresholding Algorithm）화Split Bregman등단정칙화도상복원산법상비，소제산법적복원효과화시간성능경우。
For single-regularized algorithms, there is limited prior knowledge available for image deconvolution, thus pos-ing negative effects on restored results. To overcome this drawback of single-regularized algorithms, in the framework of sparse representation, this paper proposes a new multi-regularized image deconvolution algorithm which introduces more prior knowledge to improve the quality of restored images. In the proposed algorithm, image deconvolution is cast as a global optimization problem with multiple regularized terms. To solve this complex global optimization problem, the alternating optimization and variable splitting are employed to decompose it into a sequence of subproblems. The analytical solution of u j+1 subproblem can be directly obtained due to its differentiability, whereas for non-differentiable w j+1 and v j+1 sub-problems, the proximity mapping is applied. In the experiment, three kinds of degraded images are successfully restored by the proposed algorithm, which verifies the effectiveness of it. Compared with FISTA(Fast Iterative Shrinkage-Thresh-olding Algorithm)and Split Bregman, the proposed algorithm shows better performances on restored results and speed.