Dynamic coarse‐to‐fine ISAR image blind denoising using active joint prior learning

Abstract

Most existing nonblind denoising approaches assumed the noise to be homogeneous white Gaussian distribution with known intensity. However, it is difficult to know beforehand or model accurately real-world noises with complex hybrid distribution and noise intensity. In this paper, active joint prior learning (JPL) is proposed for real-world ISAR image blind denoising. (1) To explore strong model hierarchy and components relationship automatically, a novel graphical Dirichlet mixture process (GDMP) model is developed, where the latent representations and component hyperparameters are jointly learned from each other. (2) A multiscale joint learning strategy (MJLS) is proposed to take advantage of both the optimization- and discriminative learning-based capabilities. The external noiseless, internal noisy image information and their relationships are jointly explored simultaneously. (3) Low-rank weighted sparse learning (LWSL) is proposed to learn sparse discriminative correlation components for robust prior learning, and latent low-rank embedding for GDMP patterns self-adaptive inference. Extensive experimental results on ISAR image datasets demonstrate the effectiveness of the proposed model for both synthesis and real-world noisy ISAR images, and the proposed method outperforms the state-of-the-art denoising methods.