CAJ | 학술논문

全变差模型因能有效捕捉图像与视频中的细节信息而被广泛应用于机器视觉中，曲波变换具有较强捕捉二维信号中线状跳变信息的能力。文中结合全变差模型和曲波变换的优点，提出一类能更好地捕捉二维信号特征的联合稀疏表示模型，并用原对偶算法求解该模型，即原对偶全变差曲波算法。实验结果表明，用文中模型及求解算法处理后的图像，其客观质量及主观视觉效果均优于现有算法。文中算法也可用于解决图像去模糊、超分辨率等其它具有挑战性的图像处理问题。
전변차모형인능유효포착도상여시빈중적세절신식이피엄범응용우궤기시각중，곡파변환구유교강포착이유신호중선상도변신식적능력。문중결합전변차모형화곡파변환적우점，제출일류능경호지포착이유신호특정적연합희소표시모형，병용원대우산법구해해모형，즉원대우전변차곡파산법。실험결과표명，용문중모형급구해산법처리후적도상，기객관질량급주관시각효과균우우현유산법。문중산법야가용우해결도상거모호、초분변솔등기타구유도전성적도상처리문제。
Total variation model is widely used in machine vision due to its strong ability of capturing the details of the images and the videos. Curvelet transform can capture the edges and curved lines of the 2D signals easily. Combining both advantages, a class of joint sparse representation model is proposed, i. e. total variation and curvelet ( TVC ) . This model can represent the characteristics of the 2 D signals more effectively. Primal-dual ( PD) scheme is used to solve the model, which is called PDTVC algorithm. Experimental results show that PDTVC outperforms the existing algorithms in both subjective visual effect and objective image qualities. PDTVC can be applied to various challenging image processing tasks as well, such as deblurring and super resolution.