Four-frame pixel super-resolution method for lensless imaging systems

Abstract

Lensless imaging systems have gained significant attention recently due to their advantages in terms of reduced size and weight compared to traditional lens-based systems. However, like other imaging methods, lensless imaging encounters challenges in resolving scenes with more details. In this article, we propose a novel four-frame super-resolution method specifically tailored for lensless imaging systems. Our approach shares similarities with previous lensless imaging systems, involving a sensor and a modulation device placed in front of the image sensor. We develop an explicit degradation downsampling model with sub-pixel shifts and provide the solution to corresponding inverse problem, may offering valuable guidance for other super-resolution imaging algorithms based on spatial displacements. By applying random lateral sub-pixel shifts, acquiring four low-resolution (LR) images, and fusing their spatial information, we achieve high-resolution (HR) sensor recordings, enabling super-resolution reconstruction of the imaging scene. Numerical simulations demonstrate approximately an improvement in spatial resolution compared to single-measurement methods. Furthermore, we evaluate the performance of our method across various lensless imaging systems utilizing different masks, validating its versatility and effectiveness in achieving higher resolution outcomes. Experimental results also support our proposed scheme's ability to achieve higher spatial resolution reconstruction in a real system.