  Lensless imaging has emerged as a promising field within inverse imaging,
offering compact, cost-effective solutions with the potential to revolutionize
the computational camera market. By circumventing traditional optical
components like lenses and mirrors, novel approaches like mask-based lensless
imaging eliminate the need for conventional hardware. However, advancements in
lensless image reconstruction, particularly those leveraging Generative
Adversarial Networks (GANs), are hindered by the reliance on data-driven
training processes, resulting in network specificity to the Point Spread
Function (PSF) of the imaging system. This necessitates a complete retraining
for minor PSF changes, limiting adaptability and generalizability across
diverse imaging scenarios. In this paper, we introduce a novel approach to
multi-PSF lensless imaging, employing a dual discriminator cyclic adversarial
framework. We propose a unique generator architecture with a sparse
convolutional PSF-aware auxiliary branch, coupled with a forward model
integrated into the training loop to facilitate physics-informed learning to
handle the substantial domain gap between lensless and lensed images.
Comprehensive performance evaluation and ablation studies underscore the
effectiveness of our model, offering robust and adaptable lensless image
reconstruction capabilities. Our method achieves comparable performance to
existing PSF-agnostic generative methods for single PSF cases and demonstrates
resilience to PSF changes without the need for retraining.
