IEEE Trans Image Process - Dual-geometric neighbor embedding for image super resolution with sparse tensor.

Tópicos

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Resumo

Neighbors embedding (NE) technology has proved its efficiency in single image super resolution (SISR). However, image patches do not strictly follow the similar structure in the low-resolution and high-resolution spaces, consequently leading to a bias to the image restoration. In this paper, considering that patches are a set of data with multiview characteristics and spatial organization, we advance a dual-geometric neighbor embedding (DGNE) approach for SISR. In DGNE, multiview features and local spatial neighbors of patches are explored to find a feature-spatial manifold embedding for images. We adopt a geometrically motivated assumption that for each patch there exists a small neighborhood in which only the patches that come from the same feature-spatial manifold, will lie approximately in a low-dimensional affine subspace formulated by sparse neighbors. In order to find the sparse neighbors, a tensor-simultaneous orthogonal matching pursuit algorithm is advanced to realize a joint sparse coding of feature-spatial image tensors. Some experiments are performed on realizing a 3X amplification of natural images, and the recovered results prove its efficiency and superiority to its counterparts.

Resumo Limpo

neighbor embed ne technolog prove effici singl imag super resolut sisr howev imag patch strict follow similar structur lowresolut highresolut space consequ lead bias imag restor paper consid patch set data multiview characterist spatial organ advanc dualgeometr neighbor embed dgne approach sisr dgne multiview featur local spatial neighbor patch explor find featurespati manifold embed imag adopt geometr motiv assumpt patch exist small neighborhood patch come featurespati manifold will lie approxim lowdimension affin subspac formul spars neighbor order find spars neighbor tensorsimultan orthogon match pursuit algorithm advanc realiz joint spars code featurespati imag tensor experi perform realiz x amplif natur imag recov result prove effici superior counterpart

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