Two-layer Residual Sparsifying Transform Learning for Image Reconstruction

Understand

Signal models based on sparsity, low-rank and other properties have been exploited for image reconstruction from limited and corrupted data in medical imaging and other computational imaging applications.

• In particular, sparsifying transform models have shown promise in various applications, and offer numerous advantages such as efficiencies in sparse coding and learning.
• This work investigates pre-learning a two-layer extension of the transform model for image reconstruction, wherein the transform domain or filtering residuals of the image are further sparsified in the second layer.
• The proposed block coordinate descent optimization algorithms involve highly efficient updates.