Image and Depth Estimation with Mask-Based Lensless Cameras

Abstract

Mask-based lensless cameras replace the lens by placing a fixed mask on top of an image sensor. These cameras can potentially be very thin and even flexible. Recently, it has been demonstrated that such mask-based cameras can recover light intensity and depth information of a scene. Existing depth recovery algorithms either assume that the scene consists of a small number of depth planes or solve a sparse recovery problem over a large 3D volume, and lose robustness to complicated scenes consisting of varying depth surface. In this paper, we propose a new approach for depth estimation based on alternating gradient descent algorithm that jointly estimates the continuous depth map and light distribution of a scene. The computational complexity of the algorithm scales linearly with the spatial dimension of the imaging system. We present simulation results on image and depth reconstruction for standard 3D test scenes. The comparison between the proposed algorithm and other method shows that our algorithm is faster and more robust for natural scenes with a large range of depths.