Adaptive Graph-based Total Variation for Tomographic Reconstructions
share
Sparsity exploiting image reconstruction (SER) methods have been extensively used with Total Variation (TV) regularization for tomographic reconstructions. Local TV meth- ods fail to preserve texture details and often create additional artifacts due to over-smoothing. Non-Local TV (NLTV) has been proposed as a solution to this but lacks continuous update and is computationally complex. In this paper we propose Adaptive Graph-based TV (AGT). Similar to NLTV our proposed method goes beyond spatial similarity between different regions of an image being reconstructed by establishing a connection between similar regions in the image regardless of spatial distance. How- ever, it is computationally more efficient and involves updating the graph prior during every iteration making the connection between similar regions stronger. Since TV is a special case of graph TV the proposed method can also be seen as a generalization of SER and TV methods. It promotes sparsity in the wavelet and graph gradient domains. Extensive experimentation shows that when compared to other methods we achieve a better result with AGT in every case.
READ FULL TEXT
