Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Cluster sparsity field: an internal hyperspectral imagery prior for reconstruction|
van den Hengel, A.
|Citation:||International Journal of Computer Vision, 2018; 126(8):797-821|
|Lei Zhang, Wei Wei, Yanning Zhang, Chunhua Shen, Anton van den Hengel, Qinfeng Shi|
|Abstract:||Hyperspectral images (HSIs) have significant advantages over more traditional image types for a variety of computer vision applications dues to the extra information available. The practical reality of capturing and transmitting HSIs however, means that they often exhibit large amounts of noise, or are undersampled to reduce the data volume. Methods for combating such image corruption are thus critical to many HSIs applications. Here we devise a novel cluster sparsity field (CSF) based HSI reconstruction framework which explicitly models both the intrinsic correlation between measurements within the spectrum for a particular pixel, and the similarity between pixels due to the spatial structure of the HSI. These two priors have been shown to be effective previously, but have been always considered separately. By dividing pixels of the HSI into a group of spatial clusters on the basis of spectrum characteristics, we define CSF, a Markov random field based prior. In CSF, a structured sparsity potential models the correlation between measurements within each spectrum, and a graph structure potential models the similarity between pixels in each spatial cluster. Then, we integrate the CSF prior learning and image reconstruction into a unified variational framework for optimization, which makes the CSF prior image-specific, and robust to noise. It also results in more accurate image reconstruction compared with existing HSI reconstruction methods, thus combating the effects of noise corruption or undersampling. Extensive experiments on HSI denoising and HSI compressive sensing demonstrate the effectiveness of the proposed method.|
|Keywords:||Structured sparsity; spatial similarity; hyperspectral denoising; compressive sensing|
|Description:||Published online: 21 March 2018|
|Rights:||© Springer Science+Business Media, LLC, part of Springer Nature 2018|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.