为了尽可能精确地分割出强度不均匀图像更多的细节部分,提出一种融合深度图像先验的变分图像分割模型,利用交替方向乘子法设计相应的数值求解算法。实验结果表明,提出的模型在去噪正则化(regularization by denoising,RED)框架下融合了T...为了尽可能精确地分割出强度不均匀图像更多的细节部分,提出一种融合深度图像先验的变分图像分割模型,利用交替方向乘子法设计相应的数值求解算法。实验结果表明,提出的模型在去噪正则化(regularization by denoising,RED)框架下融合了TV正则项捕获边缘和卷积神经网络(convolutional neural network,CNN)捕获细节的优势,尤其在处理结构丰富和纹理细致的图像时,可以分割出更多的细节,分割结果更精确。同时,提出的方法对于不同的初始轮廓具有很好的鲁棒性。此外,与对比实验中处理非均匀图像分割的方法相比,该模型算法复杂度低,具有快速高效的优势。展开更多
Directionality of image plays a very important role in human visual system and it is important prior information of image. In this paper we propose a weighted directional total variation model to reconstruct image fro...Directionality of image plays a very important role in human visual system and it is important prior information of image. In this paper we propose a weighted directional total variation model to reconstruct image from its finite number of noisy compressive samples. A novel self-adaption, texture preservation method is designed to select the weight. Inspired by majorization-minimization scheme, we develop an efficient algorithm to seek the optimal solution of the proposed model by minimizing a sequence of quadratic surrogate penalties. The numerical examples are performed to compare its performance with four state-of-the-art algorithms. Experimental results clearly show that our method has better reconstruction accuracy on texture images than the existing scheme.展开更多
文摘为了尽可能精确地分割出强度不均匀图像更多的细节部分,提出一种融合深度图像先验的变分图像分割模型,利用交替方向乘子法设计相应的数值求解算法。实验结果表明,提出的模型在去噪正则化(regularization by denoising,RED)框架下融合了TV正则项捕获边缘和卷积神经网络(convolutional neural network,CNN)捕获细节的优势,尤其在处理结构丰富和纹理细致的图像时,可以分割出更多的细节,分割结果更精确。同时,提出的方法对于不同的初始轮廓具有很好的鲁棒性。此外,与对比实验中处理非均匀图像分割的方法相比,该模型算法复杂度低,具有快速高效的优势。
基金the National Natural Science Foundation of China(Nos.11401318 and 11671004)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.15KJB110018)the Scientific Research Foundation of NUPT(No.NY214023)
文摘Directionality of image plays a very important role in human visual system and it is important prior information of image. In this paper we propose a weighted directional total variation model to reconstruct image from its finite number of noisy compressive samples. A novel self-adaption, texture preservation method is designed to select the weight. Inspired by majorization-minimization scheme, we develop an efficient algorithm to seek the optimal solution of the proposed model by minimizing a sequence of quadratic surrogate penalties. The numerical examples are performed to compare its performance with four state-of-the-art algorithms. Experimental results clearly show that our method has better reconstruction accuracy on texture images than the existing scheme.
