All-Optical Diffractive Operators for Rapid, Computer-Free Morphological Transformations

Morphological transformations are playing a key role in visual information processing with diverse applications ranging from bioimaging to video surveillance and environmental monitoring. However, these operations are becoming increasingly computationally intensive, requiring substantial memory and processing power as the size of image datasets expands. This paper describes a fast, highly parallel approach to perform morphological transformations by diffractive computing. These all-optical processors consist of successive diffractive surfaces designed to perform dilation and erosion operations by learning the relations between input and transformed images via a deep learning-based optimization process. Unlike existing digital methods, our free-space diffractive devices implement these transformations in a computer-free manner by directly processing the optical wavefront. The cascaded diffractive architecture further enables image denoising and flexible tuning of the extent and directionality of erosion/dilation through the same training process by adjusting target image datasets, realizing the synthesis of diverse transformation kernels on demand. We also demonstrate that the optical process is scalable and can process large volumes of visual information in a highly parallel manner. Experimentally, we realize such a diffractive network in a reflection configuration using a phase-only spatial light modulator (SLM) and perform morphological transformations on both amplitude- and phase-encoded images.