December 8, 2025Open Access

From sparse recovery to plug-and-play priors, understanding trade-offs for stable recovery with generalized projected gradient descent

View Full Paper

Ask AI

Bookmark

Authors

Discussion

Observational analysis reveals trade-offs in stability and performance for sparse recovery, suggesting improved robustness is possible.

The proposed approach improves stability while addressing trade-offs in performance during recovery tasks.
Key evidence indicates better control over stability and robustness constants in recovery processes.
Utilizing Generalized Projected Gradient Descent, approaches are adapted for structured noise like sparse outliers.
This work emphasizes the need for robust methods in recovery scenarios, highlighting practical implications for image inverse problems.

Joundi et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark