针对Retinex理论的低照度图像增强算法中光照图像估计问题,提出一种基于YCbCr颜色空间的低照度图像增强方法.该方法将原始低照度图像从RGB(Red Green Blue)颜色空间转换到YCbCr颜色空间,提取该空间中Y分量构建为原始光照图像分量L1(x,y)...针对Retinex理论的低照度图像增强算法中光照图像估计问题,提出一种基于YCbCr颜色空间的低照度图像增强方法.该方法将原始低照度图像从RGB(Red Green Blue)颜色空间转换到YCbCr颜色空间,提取该空间中Y分量构建为原始光照图像分量L1(x,y),并对L1(x,y)进行Gamma校正得到增强的光照图像分量L2(x,y),经Retinex模型得到增强图像R(x,y),采用多尺度细节增强方法对图像R(x,y)进行细节增强,得到最终增强图像Re(x,y).实验结果表明,所提方法不仅能有效提升亮度,避免亮度和色彩失真,增强了图像的细节信息并获得了更好的视觉效果,而且运行速度快.展开更多
The resolution and contrast of microscope imaging is often affected by aberrations introduced by imperfect optical systems and inhomogeneous refractive structures in specimens.Adaptive optics(AO)compensates these aber...The resolution and contrast of microscope imaging is often affected by aberrations introduced by imperfect optical systems and inhomogeneous refractive structures in specimens.Adaptive optics(AO)compensates these aberrations and restores diffraction limited performance.A wide range of AO solutions have been introduced,often tailored to a specific microscope type or application.Until now,a universal AO solution-one that can be readily transferred between microscope modalities-has not been deployed.We propose versatile and fast aberration correction using a physics-based machine learning assisted wavefront-sensorless AO control(MLAO)method.Unlike previous ML methods,we used a specially constructed neural network(NN)architecture,designed using physical understanding of the general microscope image formation,that was embedded in the control loop of different microscope systems.The approach means that not only is the resulting NN orders of magnitude simpler than previous NN methods,but the concept is translatable across microscope modalities.We demonstrated the method on a two-photon,a three-photon and a widefield three-dimensional(3D)structured illumination microscope.Results showed that the method outperformed commonly-used modal-based sensorless AO methods.We also showed that our ML-based method was robust in a range of challenging imaging conditions,such as 3D sample structures,specimen motion,low signal to noise ratio and activity-induced fluorescence fluctuations.Moreover,as the bespoke architecture encapsulated physical understanding of the imaging process,the internal NN configuration was no-longer a"black box",but provided physical insights on internal workings,which could influence future designs.展开更多
文摘针对Retinex理论的低照度图像增强算法中光照图像估计问题,提出一种基于YCbCr颜色空间的低照度图像增强方法.该方法将原始低照度图像从RGB(Red Green Blue)颜色空间转换到YCbCr颜色空间,提取该空间中Y分量构建为原始光照图像分量L1(x,y),并对L1(x,y)进行Gamma校正得到增强的光照图像分量L2(x,y),经Retinex模型得到增强图像R(x,y),采用多尺度细节增强方法对图像R(x,y)进行细节增强,得到最终增强图像Re(x,y).实验结果表明,所提方法不仅能有效提升亮度,避免亮度和色彩失真,增强了图像的细节信息并获得了更好的视觉效果,而且运行速度快.
基金supported by grants from the European Research Council(to MJB:AdOMiS,No.695140,to AMP:No.852765),Wellcome Trust(to MJB:203285/C/16/Z,to ID and MJB:107457/Z/15/Z,to AMP:204651/Z/16/Z,to HA:222807/Z/21/Z)Engineering and Physical Sciences Research Council(to MJB:EP/W024047/1)the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship,a Schmidt Futures program(to QH).
文摘The resolution and contrast of microscope imaging is often affected by aberrations introduced by imperfect optical systems and inhomogeneous refractive structures in specimens.Adaptive optics(AO)compensates these aberrations and restores diffraction limited performance.A wide range of AO solutions have been introduced,often tailored to a specific microscope type or application.Until now,a universal AO solution-one that can be readily transferred between microscope modalities-has not been deployed.We propose versatile and fast aberration correction using a physics-based machine learning assisted wavefront-sensorless AO control(MLAO)method.Unlike previous ML methods,we used a specially constructed neural network(NN)architecture,designed using physical understanding of the general microscope image formation,that was embedded in the control loop of different microscope systems.The approach means that not only is the resulting NN orders of magnitude simpler than previous NN methods,but the concept is translatable across microscope modalities.We demonstrated the method on a two-photon,a three-photon and a widefield three-dimensional(3D)structured illumination microscope.Results showed that the method outperformed commonly-used modal-based sensorless AO methods.We also showed that our ML-based method was robust in a range of challenging imaging conditions,such as 3D sample structures,specimen motion,low signal to noise ratio and activity-induced fluorescence fluctuations.Moreover,as the bespoke architecture encapsulated physical understanding of the imaging process,the internal NN configuration was no-longer a"black box",but provided physical insights on internal workings,which could influence future designs.
