Deep Learning-Based Imaging Using Single-Lens and Multi-Aperture Diffractive Optical Systems

The pressure to reduce weight and improve image quality of the imaging devices continues to push research in the area of flat optics with computational image reconstruction. This paper presents a new end-to-end framework applying two convolutional neural networks (CNNs) to reconstruct images captured with multilevel diffractive lenses (MDLs). We show that the patch-wise chromatic blur and image-wise context-aware color highlights, the distortions inherent to MDLs, can be successfully addressed with the suggested reconstruction pipeline. The generative adversarial network (GAN) is first used to remove image-wise color distortion, while a patch-wise network is then used to apply chromatic deblur. The proposed approach produces better image quality improvement than the context-independent color correction with a deconvolution-based chromatic deblur. We also show that the proposed end-to-end reconstruction is equally applicable for single-and multi-aperture MDL-based imaging systems.