  Mask-based lensless cameras can be flat, thin, and light-weight, which makes
them suitable for novel designs of computational imaging systems with large
surface areas and arbitrary shapes. Despite recent progress in lensless
cameras, the quality of images recovered from the lensless cameras is often
poor due to the ill-conditioning of the underlying measurement system. In this
paper, we propose to use coded illumination to improve the quality of images
reconstructed with lensless cameras. In our imaging model, the scene/object is
illuminated by multiple coded illumination patterns as the lensless camera
records sensor measurements. We designed and tested a number of illumination
patterns and observed that shifting dots (and related orthogonal) patterns
provide the best overall performance. We propose a fast and low-complexity
recovery algorithm that exploits the separability and block-diagonal structure
in our system. We present simulation results and hardware experiment results to
demonstrate that our proposed method can significantly improve the
reconstruction quality.
