In this paper,we propose a shear high-order gradient(SHOG)operator by combining the shear operator and high-order gradient(HOG)operator.Compared with the HOG operator,the proposed SHOG operator can incorporate more di...In this paper,we propose a shear high-order gradient(SHOG)operator by combining the shear operator and high-order gradient(HOG)operator.Compared with the HOG operator,the proposed SHOG operator can incorporate more directionality and detect more abundant edge information.Based on the SHOG operator,we extend the total variation(TV)norm to shear high-order total variation(SHOTV),and then propose a SHOTV deblurring model.We also study some properties of the SHOG operator,and show that the SHOG matrices are Block Circulant with Circulant Blocks(BCCB)when the shear angle isπ/4.The proposed model is solved efficiently by the alternating direction method of multipliers(ADMM).Experimental results demonstrate that the proposed method outperforms some state-of-the-art non-blind deblurring methods in both objective and perceptual quality.展开更多
为了提高现有块压缩感知重构算法的性能,提出了基于全变分和混合变分模型的块压缩感知(简称BCS-TV和BCS-MV)算法。该方法以块为单位进行图像采样,以自然图像正则项的稀疏性为先验条件,通过变型的增广拉格朗日交替方向乘子法(ALM-ADMM),...为了提高现有块压缩感知重构算法的性能,提出了基于全变分和混合变分模型的块压缩感知(简称BCS-TV和BCS-MV)算法。该方法以块为单位进行图像采样,以自然图像正则项的稀疏性为先验条件,通过变型的增广拉格朗日交替方向乘子法(ALM-ADMM),在整幅图像范围内逼近目标函数来重构原始图像。与以前基于一致性块采样的压缩感知工作对比,该算法的PSNR约提高1.5 d B,SSIM约提高0.05,运行速度较稳定,特别适合具有固定传输时延的多媒体数据处理场合。展开更多
In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detectio...In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detection algorithm of infrared small and dim target is proposed in this paper.Firstly,the original infrared images are changed into a new infrared patch tensor mode through data reconstruction.Then,the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics,and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness.Finally,the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image,and the final small target is worked out by a simple partitioning algorithm.The test results in various spacebased downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate.It is a kind of infrared small and dim target detection method with good performance.展开更多
基金Supported by the National Natural Science Foundation of China(61701004)Outstanding Young Talents Support Program of Anhui Province(gxyq2021178)+1 种基金Open Fund of Key Laboratory of Anhui Higher Education Institutes(CS2021-07)Program of University Mathematics Teaching Research and Development Center(CMC20200301)。
文摘In this paper,we propose a shear high-order gradient(SHOG)operator by combining the shear operator and high-order gradient(HOG)operator.Compared with the HOG operator,the proposed SHOG operator can incorporate more directionality and detect more abundant edge information.Based on the SHOG operator,we extend the total variation(TV)norm to shear high-order total variation(SHOTV),and then propose a SHOTV deblurring model.We also study some properties of the SHOG operator,and show that the SHOG matrices are Block Circulant with Circulant Blocks(BCCB)when the shear angle isπ/4.The proposed model is solved efficiently by the alternating direction method of multipliers(ADMM).Experimental results demonstrate that the proposed method outperforms some state-of-the-art non-blind deblurring methods in both objective and perceptual quality.
文摘为了提高现有块压缩感知重构算法的性能,提出了基于全变分和混合变分模型的块压缩感知(简称BCS-TV和BCS-MV)算法。该方法以块为单位进行图像采样,以自然图像正则项的稀疏性为先验条件,通过变型的增广拉格朗日交替方向乘子法(ALM-ADMM),在整幅图像范围内逼近目标函数来重构原始图像。与以前基于一致性块采样的压缩感知工作对比,该算法的PSNR约提高1.5 d B,SSIM约提高0.05,运行速度较稳定,特别适合具有固定传输时延的多媒体数据处理场合。
文摘In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detection algorithm of infrared small and dim target is proposed in this paper.Firstly,the original infrared images are changed into a new infrared patch tensor mode through data reconstruction.Then,the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics,and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness.Finally,the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image,and the final small target is worked out by a simple partitioning algorithm.The test results in various spacebased downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate.It is a kind of infrared small and dim target detection method with good performance.
