Light field images super-resolution based on sub-pixel and gradient guide
For the problem of low spatial resolution of light field images captured by light field cameras,a super-resolution method for light field images based on sub-pixel and gradient guide was proposed.A multiple sub-pixel information extraction module was designed,which divided the sub-aperture images into four image stacks:horizontal,vertical,diagonal and anti-diagonal,and extracted the sub-pixel information of each image stack separately.Meanwhile,considering that the gradient prior could provide effective clues for predicting high-frequency details,the gradient multiple sub-pixel information of the sub-aperture images was fused in the reconstruction process.Experimental results on five publicly available databases show that the proposed method not only generally outperforms the existing methods in terms of objective indexes,but also has better performance in the subjective visual effect,which the edge texture details are clearer.
super-resolutionlight field imagessub-pixel informationdeep learning
NETL
NSTL
万方数据
