For addressing impulse noise in images, this paper proposes a denoising algorithm for non-convex impulse noise images based on the l_(0) norm fidelity term. Since the total variation of the l_(0) norm has a better den...For addressing impulse noise in images, this paper proposes a denoising algorithm for non-convex impulse noise images based on the l_(0) norm fidelity term. Since the total variation of the l_(0) norm has a better denoising effect on the pulse noise, it is chosen as the model fidelity term, and the overlapping group sparse term combined with non-convex higher term is used as the regularization term of the model to protect the image edge texture and suppress the staircase effect. At the same time, the alternating direction method of multipliers, the majorization–minimization method and the mathematical program with equilibrium constraints were used to solve the model. Experimental results show that the proposed model can effectively suppress the staircase effect in smooth regions, protect the image edge details, and perform better in terms of the peak signal-to-noise ratio and the structural similarity index measure.展开更多
l_(0)梯度最小化图像平滑算法可在保持边缘的同时滤除纹理和细节,但该算法使用图像梯度判决被平滑成分时会出现包含较小图像梯度(弱边缘)的区域会被平滑,而包含较大图像梯度(强纹理)的区域被保留的现象.为克服此缺陷,提出一种基于图像块...l_(0)梯度最小化图像平滑算法可在保持边缘的同时滤除纹理和细节,但该算法使用图像梯度判决被平滑成分时会出现包含较小图像梯度(弱边缘)的区域会被平滑,而包含较大图像梯度(强纹理)的区域被保留的现象.为克服此缺陷,提出一种基于图像块l_(0)梯度最小化算法(image-patch based l_(0)gradient minimization algorithm,简称IP-l_(0)算法)的图像平滑算法,通过对输入图像中的图像块而非整幅图像进行平滑,动态改变图像块目标函数中的权重参数,令主要包含强纹理的图像块以较大的力度进行平滑,而主要包含弱边缘的图像块以较小的力度进行平滑,再整合平滑后的图像块得到整个边缘保持平滑图像.对IP-l_(0)算法、原始的l_(0)梯度最小化算法、基于局部拉普拉斯滤波器的算法、基于相对全变差算法、基于树滤波的算法,以及2种基于深度学习的边缘保持算法进行仿真实验,结果表明,使用IP-l_(0)算法滤波后的图像能在保持较弱的边缘的同时平滑强纹理.展开更多
基金funded by National Nature Science Foundation of China,grant number 61302188。
文摘For addressing impulse noise in images, this paper proposes a denoising algorithm for non-convex impulse noise images based on the l_(0) norm fidelity term. Since the total variation of the l_(0) norm has a better denoising effect on the pulse noise, it is chosen as the model fidelity term, and the overlapping group sparse term combined with non-convex higher term is used as the regularization term of the model to protect the image edge texture and suppress the staircase effect. At the same time, the alternating direction method of multipliers, the majorization–minimization method and the mathematical program with equilibrium constraints were used to solve the model. Experimental results show that the proposed model can effectively suppress the staircase effect in smooth regions, protect the image edge details, and perform better in terms of the peak signal-to-noise ratio and the structural similarity index measure.
文摘l_(0)梯度最小化图像平滑算法可在保持边缘的同时滤除纹理和细节,但该算法使用图像梯度判决被平滑成分时会出现包含较小图像梯度(弱边缘)的区域会被平滑,而包含较大图像梯度(强纹理)的区域被保留的现象.为克服此缺陷,提出一种基于图像块l_(0)梯度最小化算法(image-patch based l_(0)gradient minimization algorithm,简称IP-l_(0)算法)的图像平滑算法,通过对输入图像中的图像块而非整幅图像进行平滑,动态改变图像块目标函数中的权重参数,令主要包含强纹理的图像块以较大的力度进行平滑,而主要包含弱边缘的图像块以较小的力度进行平滑,再整合平滑后的图像块得到整个边缘保持平滑图像.对IP-l_(0)算法、原始的l_(0)梯度最小化算法、基于局部拉普拉斯滤波器的算法、基于相对全变差算法、基于树滤波的算法,以及2种基于深度学习的边缘保持算法进行仿真实验,结果表明,使用IP-l_(0)算法滤波后的图像能在保持较弱的边缘的同时平滑强纹理.
