Toward Depth Estimation Using Mask-Based Lensless Cameras

I just discovered on a new paper by M. Asif, one of the guys behind the FlatCam.

Toward Depth Estimation Using Mask-Based Lensless Cameras,

M. Asif, submitted November 9th,

(featured image extracted from Fig.1 of the manuscript)


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.

For those of you who do not know about it, the idea behind FlatCam is to extend the camera obscura principle by using a transmission mask and computational algorithms to obtain images without using lenses. This provides very compact devices. For me, it is a very good example of trading some physical elements of your optical system for post-processing algorithms. The concept can be extended to multiple regions of the electromagnetic spectrum, and also to obtain extra information (wavelength, depth), by adequate tuning of the algorithms used.