Comparison of Dictionary-Based Image Reconstruction Algorithms for Inverse Problems

Date

2020-10-07

Author

Dogan, Didem
Öktem, Sevinç Figen

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Many inverse problems in imaging involve measurements that are in the form of convolutions. Sparsity priors are widely exploited in their solutions for regularization as these problems are generally ill-posed. In this work, we develop image reconstruction methods for these inverse problems using patchbased and convolutional sparse models. The resulting regularized inverse problems are solved via the alternating direction method of multipliers (ADMM). The performance of the developed algorithms is investigated for an application in computational spectral imaging. Simulation results suggest that the convolutional sparse model provides similar reconstruction performance with the patch-based model; but the convolutional method is more advantageous in terms of computational cost.

Subject Keywords

Inverse problems, Signal to noise ratio, Imaging, Image reconstruction, Convex functions, Computational modeling, Sparse matrices

URI

https://hdl.handle.net/11511/71585

DOI

https://doi.org/10.1109/SIU49456.2020.9302165

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

D. Dogan and S. F. Öktem, “Comparison of Dictionary-Based Image Reconstruction Algorithms for Inverse Problems,” 28th Signal Processing and Communications Applications Conference (SIU) (2020), 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/71585.