TY - JOUR
T1 - Rank-One Matrix Approximation With ℓp-Norm for Image Inpainting
AU - Li, Xiao Peng
AU - Liu, Qi
AU - So, Hing Cheung
PY - 2020
Y1 - 2020
N2 - In the problem of image inpainting, one popular approach is based on low-rank matrix completion. Compared with other methods which need to convert the image into vectors or dividing the image into patches, matrix completion operates on the whole image directly. Therefore, it can preserve latent information of the two-dimensional image. An efficient method for low-rank matrix completion is to employ the matrix factorization technique. However, conventional low-rank matrix factorization-based methods often require a prespecified rank, which is challenging to determine in practice. The proposed method factorizes an image matrix as a sum of rank-one matrices so that it does not require rank information in advance as it can be automatically estimated by the algorithm itself when the algorithm has satisfactorily converged. In our study, matching pursuit is applied to search for the best rank-one matrix at each iteration. To be robust against impulsive noise, the residual error between the observed and estimated matrices is minimized by ℓp-norm with 0