Adaptive Graph-Based Total Variation for Tomographic Reconstructions

作者：Mahmood Faisal^*; Shahid Nauman; Skoglund Ulf; Vandergheynst Pierre

来源：IEEE Signal Processing Letters, 2018, 25(5): 700-704.

DOI：10.1109/LSP.2018.2816582

• Graphs
• iterative image reconstruction
• nonlocal total variation
• tomography
• total variation

Sparsity exploiting image reconstruction (SER) methods have been extensively used with total variation (TV) regularization for tomographic reconstructions. Local TV methods fail to preserve texture details and often create additional artifacts due to over-smoothing. Nonlocal TV(NLTV) methods have been proposed as a solution to this but they either lack continuous updates due to computational constraints or limit the locality to a small region. In this letter, we propose adaptive graph-based TV. The proposed method goes beyond spatial similarity between different regions of an image being reconstructed by establishing a connection between similar regions in the entire image regardless of spatial distance. As compared to NLTV, the proposed method is computationally efficient and involves updating the graph prior during every iteration making the connection between similar regions stronger. Moreover, it promotes sparsity in the wavelet and graph gradient domains. Since TV is a special case of graph TV, the proposed method can also be seen as a generalization of SER and TV methods.

• 出版日期2018-5