A Non-Local Low-Rank Approach to Enforce Integrability
Abstract of Non-Local Low-Rank Approach On MAT LAB System
We propose a new approach to enforce integrability using recent advances in non-local methods.
Our formulation consists in a sparse gradient data-fitting term to handle outliers together with a gradient-domain non-local low-rank prior.
This regularization has two main advantages:
1) the low-rank prior ensures similarity between non-local gradient patches, which helps recovering high-quality clean patches from severe outliers corruption and 2) the low-rank prior efficiently reduces dense noise as it has been shown in recent image restoration works.
We propose an efficient solver for the resulting optimization formulation using alternate minimization.
Experiments show that the new method leads to an important improvement compared with previous optimization methods and is able to efficiently handle both outliers and dense noise mixed together.
This downgrades the gradient field to a non-integrable vector field. As a result, a straightforward integration approach results in a deformed surface with various artifacts.
Conclusion
We present a new approach to surface-from-gradients using a non-local low-rank regularization. The proposed method iteratively gathers non-local patches of the corrupted vector field and applies low-rank estimation to reduce perturbations such as outliers and dense noise.
We propose an efficient alternate minimization solution to the problem using a Half-Quadratic approach.
