RBM-based joint dictionary learning for ISAR resolution enhancement

Abstract

In this study, an inverse synthetic aperture radar (ISAR) image resolution enhancement algorithm based on joint dictionary learning is proposed, by which two special sets of sparse signals called dictionaries are solved by exploiting numerous high-resolution (HR) and low-resolution (LR) ISAR images. Herein a new coupled dictionary learning algorithm based on restricted Boltzmann machine (RBM) is designed to learn a LR and a HR dictionary using LR and HR image patches. Since the echoes are equivalent to similar scattering-centre models when an object is illuminated by radar signals with same centre frequency and different bandwidth, respectively, it is reasonable to assume the object's LR ISAR image shares the same sparse representation coefficients with its HR ISAR image. When a LR ISAR image is represented sparsely with a LR dictionary, a HR ISAR images can be reconstructed based on a HR dictionary owing to the similar sparse representation coefficients. Experiment results with simulation data demonstrate the superior performance of the proposed method over other classical dictionary training algorithms.

Keywords

• boltzmann machines
• learning (artificial intelligence)
• image reconstruction
• radar imaging
• image enhancement
• image resolution
• dictionaries
• synthetic aperture radar
• image representation
• radar signals
• lr isar image shares
• hr isar image
• lr dictionary
• hr dictionary
• similar sparse representation coefficients
• classical dictionary training algorithms
• rbm-based joint dictionary
• isar resolution enhancement
• inverse synthetic aperture radar image resolution enhancement algorithm
• joint dictionary learning
• sparse signals
• coupled dictionary learning algorithm
• restricted boltzmann machine
• image patches
• similar scattering-centre models