The traditional space-invariant isotropic kernel utilized by a bilateral filter(BF)frequently leads to blurry edges and gradient reversal artifacts due to tlie existence of a large amount of outliers in the local aver...The traditional space-invariant isotropic kernel utilized by a bilateral filter(BF)frequently leads to blurry edges and gradient reversal artifacts due to tlie existence of a large amount of outliers in the local averaging window.However,the efficient and accurate cstiinatioii of space-variant k(4rnels which adapt to image structures,and the fast realization of the corresponding space-variant bilateral filtering are challenging problems.To address these problems,we present a space-variant BF(SVBF).and its linear time and error-bounded acceleration method.First,we accurately estimate spacevariant,anisotropic kernels that vary with image structures in linear time through structure tensor and mininnini spanning tree.Second,we perform SVBF in linear time using two error-bounded approximation methods,namely,low-rank tensor approximation via higher-order singular value decomposition and exponential sum approximation.Tlierefore.the proposed SVBF can efficiently achieve good edge-preserving results.We validate the advantages of the proposed filter in applications including:image denoising,image enhancement,and image focus editing.Experimental results(leinonstrate that our fast and error-bounded SVBF is superior to state-of-the-art methods.展开更多
基金the National Natural Science Foundation of China under Grant Nos.61620106003.61701235,61772523,61471338 and 61571046the Beijing Natural Science Foundation of China under Grant,No.LI82059+1 种基金the Fundamental Research Funds for the Central Universities of China under Grant No.30917011323the Open Projects Program of National Laboratory of Pattern Recognition of China under Grant No.201900020.
文摘The traditional space-invariant isotropic kernel utilized by a bilateral filter(BF)frequently leads to blurry edges and gradient reversal artifacts due to tlie existence of a large amount of outliers in the local averaging window.However,the efficient and accurate cstiinatioii of space-variant k(4rnels which adapt to image structures,and the fast realization of the corresponding space-variant bilateral filtering are challenging problems.To address these problems,we present a space-variant BF(SVBF).and its linear time and error-bounded acceleration method.First,we accurately estimate spacevariant,anisotropic kernels that vary with image structures in linear time through structure tensor and mininnini spanning tree.Second,we perform SVBF in linear time using two error-bounded approximation methods,namely,low-rank tensor approximation via higher-order singular value decomposition and exponential sum approximation.Tlierefore.the proposed SVBF can efficiently achieve good edge-preserving results.We validate the advantages of the proposed filter in applications including:image denoising,image enhancement,and image focus editing.Experimental results(leinonstrate that our fast and error-bounded SVBF is superior to state-of-the-art methods.
