FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements
This work addresses the challenge of enabling ultra-miniature cameras for applications like mobile devices or medical imaging, though it is incremental as it builds on existing lensless imaging frameworks.
The paper tackles the problem of noisy and poor-quality image reconstruction in lensless cameras by proposing FlatNet, a non-iterative deep learning approach that achieves orders of magnitude improvement in image quality, producing photorealistic reconstructions from lensless measurements.
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.