In this internship, I explore different optimization algorithms for lensless imaging. Lensless imaging is a new imaging technique that replaces the lens of a camera with a diffuser mask. This allows for simpler and cheaper camera hardware. However, the reconstruction of the image from the sensor measurement is a complex ill-posed problem. The goal of this internship is to improve current reconstruction algorithms for lensless imaging. I first implemented unrolled optimization as a low computational cost way of increasing reconstruction quality. I also explored the possibility of using a plug-and-play algorithm for lensless imaging. Then, I introduced a pre-/post- denoising scheme mixing unrolled optimization and more traditional deep learning which improved current state-of-the-art result on the DiffuserCam Lensless Mirflickr Dataset. Finally, I introduce a framework for learning the mask pattern of the lensless camera jointly with the reconstruction algorithm, allowing for further improvements.