Building Layout Reconstruction Based on Complex Correntropy Criterion Under Impulsive Noise

This paper considers the building layout reconstruction (BLR) problem based on compressive sensing (CS) framework in the impulsive noise environment. Specifically, first, a CS-based imaging model considering the extended characteristics of walls is established. To realize effective BLR under impulsive noise, we formulate an optimization problem integrated maximum complex correntropy criterion (MCCC) and sparsity constraint. Then, a proximal-gradient-based iterative algorithm is introduced to solve the optimization problem. Finally, simulations under Gaussian noise and impulsive noise validate the effectiveness of the proposed method.