. One of the applications of the proposed end-to-end computational camera design paradigm is achromatic extended depth of field. When capturing an image with a regular singlet lens (top left), out-of-focus regions are blurry and chromatic aberrations further degrade the image quality. With our framework, we optimize the profile of a refractive optical element that achieves both depth and chromatic invariance. This element is fabricated using diamond turning (right) or using photolithography. After processing an image recorded with this optical element using a simple Wiener deconvolution, we obtain an all-in-focus image with little chromatic aberrations (top center). Point spread functions for both the regular lens and the optimized optical element are shown in the bottom. In this paper, we explore several applications that demonstrate the efficacy of our novel approach to domain-specific computational camera design. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. In typical cameras the optical system is designed first; once it is fixed, the parameters in the image processing algorithm are tuned to get good image reproduction. In contrast to this sequential design approach, we consider joint optimization of an optical system (for example, the physical shape of the lens) together with the parameters of the reconstruction algorithm. We build a fully-differentiable simulation model that maps the true source image to the reconstructed one. The model includes diffractive light propagation, depth and wavelength-dependent effects, noise and nonlinearities, and the image post-processing. We jointly optimize the optical parameters and the image processing algorithm parameters so as to minimize the deviation between the true and reconstructed image, over a large set of images. We implement our joint optimization method using autodifferentiation to efficiently compute parameter gradients in a stochastic optimization algorithm. We demonstrate the efficacy of this approach by applying it to achromatic extended depth of field and snapshot super-resolution imaging.
REF proposed an end-to-end optimization method of diffractive optical elements by adopting a gradient-based optimization framework.
End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging
TOGS
Computer Science