An improved anchor neighborhood regression SR method based on low-rank constraint

At present, the image super-resolution (SR) method based on sparse representation has the problem that the reconstruction speed and quality are difficult to be achieved simultaneously. Therefore, this paper proposes an improved anchor neighborhood regression SR algorithm based on low-rank constraint. Firstly, considering the critical role of locality in nonlinear data learning, the locally weighted regularization weight is introduced in the calculation of the projection matrix, which can constrain the projection process according to the correlation between the anchor point and the atoms in the corresponding neighborhood. Then, in the reconstruction phase, based on the assumption of low-rank between similar blocks, further constraints are made on the reconstruction blocks to obtain better reconstruction image quality. Experiments show that our method can not only reconstruct more image details but also achieve better reconstruction speed. Compared with some state-of-the-art sparse representation method, it achieves better reconstruction results in objective evaluation criteria.

Automated summary

This paper proposes an improved SR algorithm based on low-rank constraint, which achieves better image reconstruction quality and speed by introducing locally weighted regularization and constraints on reconstruction blocks.