Moderate resolution imaging spectroradiometer(MODIS) imaging has various applications in the field of ground monitoring,cloud classification and meteorological research.However,the limitations of the sensors and exter...Moderate resolution imaging spectroradiometer(MODIS) imaging has various applications in the field of ground monitoring,cloud classification and meteorological research.However,the limitations of the sensors and external disturbance make the resolution of image still limited in a certain level.The goal of this paper is to use a single image super-resolution(SISR) method to predict a high-resolution(HR) MODIS image from a single low-resolution(LR) input.Recently,although the method based on sparse representation has tackled the ill-posed problem effectively,two fatal issues have been ignored.First,many methods ignore the relationships among patches,resulting in some unfaithful output.Second,the high computational complexity of sparse coding using l_1 norm is needed in reconstruction stage.In this work,we discover the semantic relationships among LR patches and the corresponding HR patches and group the documents with similar semantic into topics by probabilistic Latent Semantic Analysis(p LSA).Then,we can learn dual dictionaries for each topic in the low-resolution(LR) patch space and high-resolution(HR) patch space and also pre-compute corresponding regression matrices for dictionary pairs.Finally,for the test image,we infer locally which topic it corresponds to and adaptive to select the regression matrix to reconstruct HR image by semantic relationships.Our method discovered the relationships among patches and pre-computed the regression matrices for topics.Therefore,our method can greatly reduce the artifacts and get some speed-up in the reconstruction phase.Experiment manifests that our method performs MODIS image super-resolution effectively,results in higher PSNR,reconstructs faster,and gets better visual quality than some current state-of-art methods.展开更多
This paper presents a set of multicomponent matrix Lie algebra, which is used to construct a new loop algebra A^-M. By using the Tu scheme, a Liouville integrable multicomponent equation hierarchy is generated, which ...This paper presents a set of multicomponent matrix Lie algebra, which is used to construct a new loop algebra A^-M. By using the Tu scheme, a Liouville integrable multicomponent equation hierarchy is generated, which possesses the Hamiltonian structure. As its reduction cases, the multicomponent （2＋1）-dimensional Glachette-Johnson （G J） hierarchy is given. Finally, the super-integrable coupling system of multicomponent （2＋1）-dimensional GJ hierarchy is established through enlarging the spectral problem.展开更多
基金partially supported by the National Natural Science Foundation of China (61471212)Natural Science Foundation of Zhejiang Province (LY16F010001)Natural Science Foundation of Ningbo (2016A610091, 2017A610297)
文摘Moderate resolution imaging spectroradiometer(MODIS) imaging has various applications in the field of ground monitoring,cloud classification and meteorological research.However,the limitations of the sensors and external disturbance make the resolution of image still limited in a certain level.The goal of this paper is to use a single image super-resolution(SISR) method to predict a high-resolution(HR) MODIS image from a single low-resolution(LR) input.Recently,although the method based on sparse representation has tackled the ill-posed problem effectively,two fatal issues have been ignored.First,many methods ignore the relationships among patches,resulting in some unfaithful output.Second,the high computational complexity of sparse coding using l_1 norm is needed in reconstruction stage.In this work,we discover the semantic relationships among LR patches and the corresponding HR patches and group the documents with similar semantic into topics by probabilistic Latent Semantic Analysis(p LSA).Then,we can learn dual dictionaries for each topic in the low-resolution(LR) patch space and high-resolution(HR) patch space and also pre-compute corresponding regression matrices for dictionary pairs.Finally,for the test image,we infer locally which topic it corresponds to and adaptive to select the regression matrix to reconstruct HR image by semantic relationships.Our method discovered the relationships among patches and pre-computed the regression matrices for topics.Therefore,our method can greatly reduce the artifacts and get some speed-up in the reconstruction phase.Experiment manifests that our method performs MODIS image super-resolution effectively,results in higher PSNR,reconstructs faster,and gets better visual quality than some current state-of-art methods.
基金supported by the National Key Basic Research Development of China (Grant No 2004CB318000)
文摘This paper presents a set of multicomponent matrix Lie algebra, which is used to construct a new loop algebra A^-M. By using the Tu scheme, a Liouville integrable multicomponent equation hierarchy is generated, which possesses the Hamiltonian structure. As its reduction cases, the multicomponent （2＋1）-dimensional Glachette-Johnson （G J） hierarchy is given. Finally, the super-integrable coupling system of multicomponent （2＋1）-dimensional GJ hierarchy is established through enlarging the spectral problem.
