Most existing light field(LF)super-resolution(SR)methods either fail to fully use angular information or have an unbalanced performance distribution because they use parts of views.To address these issues,we propose a...Most existing light field(LF)super-resolution(SR)methods either fail to fully use angular information or have an unbalanced performance distribution because they use parts of views.To address these issues,we propose a novel integration network based on macro-pixel representation for the LF SR task,named MPIN.Restoring the entire LF image simultaneously,we couple the spatial and angular information by rearranging the four-dimensional LF image into a two-dimensional macro-pixel image.Then,two special convolutions are deployed to extract spatial and angular information,separately.To fully exploit spatial-angular correlations,the integration resblock is designed to merge the two kinds of information for mutual guidance,allowing our method to be angular-coherent.Under the macro-pixel representation,an angular shuffle layer is tailored to improve the spatial resolution of the macro-pixel image,which can effectively avoid aliasing.Extensive experiments on both synthetic and real-world LF datasets demonstrate that our method can achieve better performance than the state-of-the-art methods qualitatively and quantitatively.Moreover,the proposed method has an advantage in preserving the inherent epipolar structures of LF images with a balanced distribution of performance.展开更多
基金Project supported by the National Natural Science Foundation of China(No.61773295)。
文摘Most existing light field(LF)super-resolution(SR)methods either fail to fully use angular information or have an unbalanced performance distribution because they use parts of views.To address these issues,we propose a novel integration network based on macro-pixel representation for the LF SR task,named MPIN.Restoring the entire LF image simultaneously,we couple the spatial and angular information by rearranging the four-dimensional LF image into a two-dimensional macro-pixel image.Then,two special convolutions are deployed to extract spatial and angular information,separately.To fully exploit spatial-angular correlations,the integration resblock is designed to merge the two kinds of information for mutual guidance,allowing our method to be angular-coherent.Under the macro-pixel representation,an angular shuffle layer is tailored to improve the spatial resolution of the macro-pixel image,which can effectively avoid aliasing.Extensive experiments on both synthetic and real-world LF datasets demonstrate that our method can achieve better performance than the state-of-the-art methods qualitatively and quantitatively.Moreover,the proposed method has an advantage in preserving the inherent epipolar structures of LF images with a balanced distribution of performance.
