Gradient-based low rank method and its application in image inpainting

Abstract

Conventional inpainting methods generally apply textures that are most similar to the areas around the missing region or use large image database. Recently, low rank property of data shows that the non-convex optimization decreases measurements. In this paper, we propose a new image prior, which implies the low rank prior knowledge of image gradients. The proposed detail-preserving image inpainting algorithm adopts the low rank regularization to gradient similarity minimization, termed gradient-based low rank approximation (Grad-LR), namely that we employ the low rank constraints in the horizontal and vertical gradients of the image and then reconstruct the desired image using the adaptive iterative singular-value thresholding of both derivatives. In the method, by incorporating the spatially adaptive iterative singular-value thresholding (SAIST) to optimize our gradient scheme, the deterministic annealing iterates the procedure efficiently. As a result, the strength of the algorithm is obvious when filling large missing region. Experimental results consistently demonstrate that the proposed algorithm works well for both structural and texture images and outperforms other techniques, in terms of both objective and subjective performance measures.