FlatNet: Towards Photorealistic Scene Reconstruction From Lensless Measurements.

ABSTRACT: 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 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 t

SUBMITTER: Khan SS 

PROVIDER: S-EPMC8979921 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature