  Lensless imaging has emerged as a potential solution towards realizing
ultra-miniature cameras by eschewing the bulky lens in a traditional camera.
Without a focusing lens, the lensless cameras rely on computational algorithms
to recover the scenes from multiplexed measurements. However, the current
iterative-optimization-based reconstruction algorithms produce noisier and
perceptually poorer images. In this work, we propose a non-iterative deep
learning based reconstruction approach that results in orders of magnitude
improvement in image quality for lensless reconstructions. Our approach, called
$\textit{FlatNet}$, lays down a framework for reconstructing high-quality
photorealistic images from mask-based lensless cameras, where the camera's
forward model formulation is known. FlatNet consists of two stages: (1) an
inversion stage that maps the measurement into a space of intermediate
reconstruction by learning parameters within the forward model formulation, and
(2) a perceptual enhancement stage that improves the perceptual quality of this
intermediate reconstruction. These stages are trained together in an end-to-end
manner. We show high-quality reconstructions by performing extensive
experiments on real and challenging scenes using two different types of
lensless prototypes: one which uses a separable forward model and another,
which uses a more general non-separable cropped-convolution model. Our
end-to-end approach is fast, produces photorealistic reconstructions, and is
easy to adopt for other mask-based lensless cameras.
