Category: IEEE 2017

Low-Rank Neighbor Embedding for Single Image Super-Resolution

Low-Rank Neighbor Embedding for Single Image Super-Resolution Abstract: This letter proposes a novel single image super-resolution (SR) method based on the low-rank matrix recovery (LRMR) and neighbor embedding (NE). LRMR is used to explore the underlying structures of subspaces spanned by similar patches. Specifically, the training patches are first divided into groups. Then the LRMR […]


Images as Occlusions of Textures A Framework for Segmentation

Images as Occlusions of Textures: A Framework for Segmentation Abstract: We propose a new mathematical and algorithmic framework for unsupervised image segmentation, which is a critical step in a wide variety of image processing applications. We have found that most existing segmentation methods are not successful on histopathology images, which prompted us to investigate segmentation […]