为了有效滤除樱桃图像在获取过程中混杂的不同噪声,保障图像识别与机器自动采摘时良好的图像信息质量,提出一种改进三维块匹配滤波(block-matching and 3D filtering, BM3D)的图像去噪方法.首先,在三维块匹配滤波的基础估计阶段构建自...为了有效滤除樱桃图像在获取过程中混杂的不同噪声,保障图像识别与机器自动采摘时良好的图像信息质量,提出一种改进三维块匹配滤波(block-matching and 3D filtering, BM3D)的图像去噪方法.首先,在三维块匹配滤波的基础估计阶段构建自适应中值滤波处理器,滤除图像中部分椒盐噪声,并改进优化硬阈值、滑窗步长及三维硬阈值等关键参数快速滤除高斯噪声;其次,在基础估计阶段与最终估计阶段之间引入中值滤波,最大限度地去除图像中剩余的混合噪声;最后,通过仿真实验验证所提算法的有效性,并对比分析改进前后算法的归一化均方误差、峰值信噪比、信噪比改善因子及结构相似性等性能.结果表明,改进的BM3D方法在保持好樱桃图像细节信息的同时,能有效去除高斯噪声和滤除大概率椒盐噪声,且随混合噪声干扰的增强,所提算法的去噪性能更佳且优于其他滤波方法.展开更多
Due to the huge difference of noise distribution,the result of a mixture of multiple noises becomes very complicated.Under normal circumstances,the most common type of mixed noise is to add impulse noise(IN)and then w...Due to the huge difference of noise distribution,the result of a mixture of multiple noises becomes very complicated.Under normal circumstances,the most common type of mixed noise is to add impulse noise(IN)and then white Gaussian noise(AWGN).From the reduction of cascaded IN and AWGN to the latest sparse representation,a great deal of methods has been proposed to reduce this form of mixed noise.However,when the mixed noise is very strong,most methods often produce a lot of artifacts.In order to solve the above problems,we propose a method based on residual learning for the removal of AWGN-IN noise in this paper.By training,our model can obtain stable nonlinear mapping from the images with mixed noise to the clean images.After a series of experiments under different noise settings,the results show that our method is obviously better than the traditional sparse representation and patch based method.Meanwhile,the time of model training and image denoising is greatly reduced.展开更多
文摘为了有效滤除樱桃图像在获取过程中混杂的不同噪声,保障图像识别与机器自动采摘时良好的图像信息质量,提出一种改进三维块匹配滤波(block-matching and 3D filtering, BM3D)的图像去噪方法.首先,在三维块匹配滤波的基础估计阶段构建自适应中值滤波处理器,滤除图像中部分椒盐噪声,并改进优化硬阈值、滑窗步长及三维硬阈值等关键参数快速滤除高斯噪声;其次,在基础估计阶段与最终估计阶段之间引入中值滤波,最大限度地去除图像中剩余的混合噪声;最后,通过仿真实验验证所提算法的有效性,并对比分析改进前后算法的归一化均方误差、峰值信噪比、信噪比改善因子及结构相似性等性能.结果表明,改进的BM3D方法在保持好樱桃图像细节信息的同时,能有效去除高斯噪声和滤除大概率椒盐噪声,且随混合噪声干扰的增强,所提算法的去噪性能更佳且优于其他滤波方法.
基金supported in part by the National Natural Science Foundation of China under Grant 61601235,in part by the Natural Science Foundation of Jiangsu Province of China under Grant BK20160972.
文摘Due to the huge difference of noise distribution,the result of a mixture of multiple noises becomes very complicated.Under normal circumstances,the most common type of mixed noise is to add impulse noise(IN)and then white Gaussian noise(AWGN).From the reduction of cascaded IN and AWGN to the latest sparse representation,a great deal of methods has been proposed to reduce this form of mixed noise.However,when the mixed noise is very strong,most methods often produce a lot of artifacts.In order to solve the above problems,we propose a method based on residual learning for the removal of AWGN-IN noise in this paper.By training,our model can obtain stable nonlinear mapping from the images with mixed noise to the clean images.After a series of experiments under different noise settings,the results show that our method is obviously better than the traditional sparse representation and patch based method.Meanwhile,the time of model training and image denoising is greatly reduced.
