In this paper, we propose a novel low-rank based non-local image denoising method for HEVC video compression with the strategy of gathering non-local patches in the rectified domain. Owing to the irreversible quantization, image compression can be considered as adding noises into the original image, causing the distortion between the original image and the de-compressed image. Current non-local collaboration based image denoising methods collect K-nearest patches from image interior with the underlying translational motion model, and restores the corrupted content from the similar image patches. However, these methods are usually not suitable for image content with repeated patterns with different scales due to the viewing perspective. To address this problem, we derive the plane orientation utilizing vanishing points, which can be determined through line segment detection and clustering. Vanishing points are used to construct a specific homographic transformation to model patch correspondence in rectified domain. With this transformation, the regularity in image can be extracted based on SIFT features and mean-shift clustering in the rectified domain, and can provide useful guidance for patch gathering. Thus, our method is especially suitable for urban building scenes. Our experimental results show that the non-local denoising in rectified domain can further improve the average PSNR comparing to those non-local methods on Urban100 dataset compressed by HEVC standard.