MARLow: A Joint Multiplanar Autoregressive and Low-Rank Approach for Image Completion
This work addresses image inpainting for computer vision applications, offering a novel method that improves accuracy in high-missing-rate scenarios.
The paper tackles image completion from random sampling by proposing a joint multiplanar autoregressive and low-rank approach (MARLow) that exploits cross-dimensional correlations and nonlocal self-similarity, achieving significant performance improvements over state-of-the-art methods even at 90% pixel missing rates.
In this paper, we propose a novel multiplanar autoregressive (AR) model to exploit the correlation in cross-dimensional planes of a similar patch group collected in an image, which has long been neglected by previous AR models. On that basis, we then present a joint multiplanar AR and low-rank based approach (MARLow) for image completion from random sampling, which exploits the nonlocal self-similarity within natural images more effectively. Specifically, the multiplanar AR model constraints the local stationarity in different cross-sections of the patch group, while the low-rank minimization captures the intrinsic coherence of nonlocal patches. The proposed approach can be readily extended to multichannel images (e.g. color images), by simultaneously considering the correlation in different channels. Experimental results demonstrate that the proposed approach significantly outperforms state-of-the-art methods, even if the pixel missing rate is as high as 90%.