Based on the hyper-Laplacian distribution of errors between the upsampled multispectral image and the ground-truth high resolution multispectral image on gradient domain, we formulatea l(p)(0 < p < 1)-norm term to more reasonably describe the relation of these two datasets. In addition, we ...
The heavy-tailed distribution of gradients in natural scen es have proven effective priors for a range of problems such as denoising, deblurring and super-resolution. These distributions are well modeled by a hyper-Laplacian p(x) ∝ ek|x| , typ- ically with 0.5 ≤α≤ 0.8. Howe...
Meanwhile gradients in the whole image satisfy heavy tailed distribution, which can be well depicted by hyper-laplacian. The unified framework could benefit from the local patch-based prior and non-local gradient sparsity prior. The proposed method is extensively evaluated on Berkeley Segmentation ...
超拉普拉斯运动模糊贝叶斯理论图像反卷积长尾分布This paper first analyzed the heavy-tailed distribution of gradients in natural scenes and proposed an algorithm about motion deblurring based on hyper-Laplacian moldel.It adopted an alternating minimization scheme to optimize the energy equation.Used a look-...
Image restorationHyper-Laplacian priorMaximum a posterior (MAP)The heavy-tailed hyper-Laplacian prior has been successfully applied in image restoration tasks, in which the unified distribution is adopted for the whole image. However, the gradient distribution......
Based on the hyper-Laplacian distribution of errors between the upsampled multispectral image and the ground-truth high resolution multispectral image on gradient domain, we formulate a ℓp(0 p p subproblem is handled by an efficient generalized shrinkage/thresholding algorithm. Finally, ...
A Novel Method for Hyper Spectral Image Classification using CNN based Laplacian Eigen map Pixels Distribution FlowThe problems of under classification, in hyperspectral imagery (HSI) and the high complexity of computing Eigen value problem for searching the nearest neighbouring pixel still exist in the...