To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects

Abstract

Privacy protection is a growing concern in the digital era, with machine vision techniques widely used throughout public and private settings. Existing methods address this growing problem by, e.g., encrypting camera images or obscuring/blurring the imaged information through digital algorithms. Here, we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects. This diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of objects positioned at its input field-of-view. After their fabrication, the thin diffractive layers collectively perform optical mode filtering to accurately form images of the objects that belong to a target data class or group of classes, while instantaneously erasing objects of the other data classes at the output field-of-view. Using the same framework, we also demonstrate the design of class-specific permutation cameras, where the objects of a target data class are pixel-wise permuted for all-optical class-specific encryption, while the other objects are irreversibly erased from the output image. The success of class-specific diffractive cameras was experimentally demonstrated using terahertz (THz) waves and 3D-printed diffractive layers that selectively imaged only one class of the MNIST handwritten digit dataset, all-optically erasing the other handwritten digits. This diffractive camera design can be scaled to different parts of the electromagnetic spectrum, including, e.g., the visible and infrared wavelengths, to provide transformative opportunities for privacy-preserving digital cameras and task-specific data-efficient imaging.

31 Pages, 7 Figures

References in corpus (4)

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Misalignment Resilient Diffractive Optical Networks
• Privacy Protection in Street-View Panoramas using Depth and Multi-View Imagery
• All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfaces

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• Integration of Programmable Diffraction with Digital Neural Networks
• Universal point spread function engineering for 3D optical information processing
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• Broadband Unidirectional Visible Imaging Using Wafer-Scale Nano-Fabrication of Multi-Layer Diffractive Optical Processors
• Optimizing Structured Surfaces for Diffractive Waveguides
• Coherence Awareness in Diffractive Neural Networks
• Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks