Toward Depth Estimation Using Mask-Based Lensless Cameras
This work addresses depth sensing for compact imaging systems, but it is incremental as it builds on existing lensless camera technology.
The paper tackled the problem of estimating depth and intensity from lensless cameras using coded masks, presenting a model and greedy algorithm that achieved depth estimation within the camera's field-of-view through simulations.
Recently, coded masks have been used to demonstrate a thin form-factor lensless camera, FlatCam, in which a mask is placed immediately on top of a bare image sensor. In this paper, we present an imaging model and algorithm to jointly estimate depth and intensity information in the scene from a single or multiple FlatCams. We use a light field representation to model the mapping of 3D scene onto the sensor in which light rays from different depths yield different modulation patterns. We present a greedy depth pursuit algorithm to search the 3D volume and estimate the depth and intensity of each pixel within the camera field-of-view. We present simulation results to analyze the performance of our proposed model and algorithm with different FlatCam settings.