Far-Field Subwavelength Acoustic Imaging by Deep Learning

Abstract

Seeing and recognizing an object whose size is much smaller than the illumination wavelength is a challenging task for an observer placed in the far field, due to the diffraction limit. Recent advances in near- and far-field microscopy have offered several ways to overcome this limitation; however, they often use invasive markers and require intricate equipment with complicated image postprocessing. On the other hand, a simple marker-free solution for high-resolution imaging may be found by exploiting resonant metamaterial lenses that can convert the subwavelength image information contained in the near field of the object to propagating field components that can reach the far field. Unfortunately, resonant metalenses are inevitably sensitive to absorption losses, which has so far largely hindered their practical applications. Here, we solve this vexing problem and show that this limitation can be turned into an advantage when metalenses are combined with deep learning techniques. We demonstrate that combining deep learning with lossy metalenses allows recognizing and imaging largely subwavelength features directly from the far field. Our acoustic learning experiment shows that, despite being 30 times smaller than the wavelength of sound, the fine details of images can be successfully reconstructed and recognized in the far field, which is crucially favored by the presence of absorption. We envision applications in acoustic image analysis, feature detection, object classification, or as a novel noninvasive acoustic sensing tool in biomedical applications.