Due to the phenomena of huge illumination changes, partial occlusion and damage between images, robust and efficient image alignment is still a challenging task. An improved online image alignment algorithm is proposed. Firstly, the Principal Component Analysis(PCA) of image gradient orientations(IGO) is utilized to provide a lower dimensional subspace which is more reliable than the intensity of pixels. The alignment is sought in the IGO domain such that the aligned IGO of the newly arrived image can be decomposed as the sum of a sparse error and a linear composition of the IGO-PCA basis learned from previously well-aligned ones. The image alignment problem can be modeled as a norm minimization problem. The problem is relaxed to a convex optimization problem in this paper, and a convex optimization algorithm based on multiplier alternating direction method is proposed. IGO-PCA basis are adaptively updated based on incremental singular value decomposition considering the migration of IGO mean in this paper. The effectiveness of the proposed algorithm is validated on a large number of challenging data sets. The experimental results show that compared with the current typical SIFT algorithm, Robust Alignment by Sparse and Low-rank decomposition(RASL) algorithm and transformed Grassmannian Robust Adaptive Subspace Tracking Algorithm(t-GRASTA), the alignment effect of the proposed algorithm is the best, and it has the strongest robustness to illumination changes and occlusion phenomena of images.