Image smoothing is a crucial image processing topic and has wide applications. For images with rich texture, most of the existing image smoothing methods are difficult to obtain significant texture removal performance...Image smoothing is a crucial image processing topic and has wide applications. For images with rich texture, most of the existing image smoothing methods are difficult to obtain significant texture removal performance because texture containing obvious edges and large gradient changes is easy to be preserved as the main edges. In this paper, we propose a novel framework （DSHFG） for image smoothing combined with the constraint of sparse high frequency gradient for texture images. First, we decompose the image into two components： a smooth component （constant component） and a non-smooth （high frequency） component. Second, we remove the non-smooth component containing high frequency gradient and smooth the other component combining with the constraint of sparse high frequency gradient. Experimental results demonstrate the proposed method is more competitive on efficiently texture removing than the state-of-the-art methods. What is more, our approach has a variety of applications including edge detection, detail magnification, image abstraction, and image composition.展开更多
Smoothing images,especially with rich texture,is an important problem in computer vision.Obtaining an ideal result is difficult due to complexity,irregularity,and anisotropicity of the texture.Besides,some properties ...Smoothing images,especially with rich texture,is an important problem in computer vision.Obtaining an ideal result is difficult due to complexity,irregularity,and anisotropicity of the texture.Besides,some properties are shared by the texture and the structure in an image.It is a hard compromise to retain structure and simultaneously remove texture.To create an ideal algorithm for image smoothing,we face three problems.For images with rich textures,the smoothing effect should be enhanced.We should overcome inconsistency of smoothing results in different parts of the image.It is necessary to create a method to evaluate the smoothing effect.We apply texture pre-removal based on global sparse decomposition with a variable smoothing parameter to solve the first two problems.A parametric surface constructed by an improved Bessel method is used to determine the smoothing parameter.Three evaluation measures:edge integrity rate,texture removal rate,and gradient value distribution are proposed to cope with the third problem.We use the alternating direction method of multipliers to complete the whole algorithm and obtain the results.Experiments show that our algorithm is better than existing algorithms both visually and quantitatively.We also demonstrate our method’s ability in other applications such as clip-art compression artifact removal and content-aware image manipulation.展开更多
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61373078, 61572292, 61602277, and 61332015, the Key Project of National Natural Science Foundation of China Joint Fund with Zhejiang Integration of Informatization and Industrialization under Grant No. U1609218, and the Natural Science Foundation of Shandong Province of China under Grant No. ZR2016FQ12.
文摘Image smoothing is a crucial image processing topic and has wide applications. For images with rich texture, most of the existing image smoothing methods are difficult to obtain significant texture removal performance because texture containing obvious edges and large gradient changes is easy to be preserved as the main edges. In this paper, we propose a novel framework （DSHFG） for image smoothing combined with the constraint of sparse high frequency gradient for texture images. First, we decompose the image into two components： a smooth component （constant component） and a non-smooth （high frequency） component. Second, we remove the non-smooth component containing high frequency gradient and smooth the other component combining with the constraint of sparse high frequency gradient. Experimental results demonstrate the proposed method is more competitive on efficiently texture removing than the state-of-the-art methods. What is more, our approach has a variety of applications including edge detection, detail magnification, image abstraction, and image composition.
基金This work was supported by NSFC Joint Fund with Zhejiang Integration of Informatization and Industrialization under Key Project(U1609218).
文摘Smoothing images,especially with rich texture,is an important problem in computer vision.Obtaining an ideal result is difficult due to complexity,irregularity,and anisotropicity of the texture.Besides,some properties are shared by the texture and the structure in an image.It is a hard compromise to retain structure and simultaneously remove texture.To create an ideal algorithm for image smoothing,we face three problems.For images with rich textures,the smoothing effect should be enhanced.We should overcome inconsistency of smoothing results in different parts of the image.It is necessary to create a method to evaluate the smoothing effect.We apply texture pre-removal based on global sparse decomposition with a variable smoothing parameter to solve the first two problems.A parametric surface constructed by an improved Bessel method is used to determine the smoothing parameter.Three evaluation measures:edge integrity rate,texture removal rate,and gradient value distribution are proposed to cope with the third problem.We use the alternating direction method of multipliers to complete the whole algorithm and obtain the results.Experiments show that our algorithm is better than existing algorithms both visually and quantitatively.We also demonstrate our method’s ability in other applications such as clip-art compression artifact removal and content-aware image manipulation.
