Compressive Sensing of Large-Scale Images: An Assumption-Free Approach
This work addresses the problem of efficient compressive sensing for large-scale images, which is incremental as it builds on existing methods like fixed point continuation and weighted LASSO with tree structure sparsity.
The paper tackles the challenge of cost-efficient compressive sensing for large-scale images by proposing a new method that is free from assumptions and restrictions, achieving high-quality reconstruction results as verified through simulations and comparisons with state-of-the-art algorithms.
Cost-efficient compressive sensing of big media data with fast reconstructed high-quality results is very challenging. In this paper, we propose a new large-scale image compressive sensing method, composed of operator-based strategy in the context of fixed point continuation method and weighted LASSO with tree structure sparsity pattern. The main characteristic of our method is free from any assumptions and restrictions. The feasibility of our method is verified via simulations and comparisons with state-of-the-art algorithms.