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.

翻译：无透镜成像作为逆向成像领域的一个新兴方向，凭借其紧凑、经济的特性，展现出革新计算相机市场的潜力。通过规避传统光学元件（如透镜和镜片），基于掩模的无透镜成像等新方法摆脱了对常规硬件的依赖。然而，无透镜图像重建技术的进展，尤其是那些利用生成对抗网络的方法，受限于对数据驱动训练过程的依赖，导致网络对成像系统的点扩散函数具有特异性。即使点扩散函数发生微小变化，也需要对网络进行完整的重新训练，这限制了模型在不同成像场景下的适应性和泛化能力。本文提出了一种新颖的多点扩散函数无透镜成像方法，采用双判别器循环对抗框架。我们设计了一种独特的生成器架构，其中包含一个稀疏卷积的点扩散函数感知辅助分支，并结合集成于训练循环中的前向模型，以促进物理信息学习，从而处理无透镜图像与有透镜图像之间的显著域差异。全面的性能评估与消融实验证明了我们模型的有效性，其提供了鲁棒且适应性强的无透镜图像重建能力。在单点扩散函数场景下，我们的方法达到了与现有点扩散函数无关生成方法相当的性能，并在无需重新训练的情况下展现出对点扩散函数变化的强适应能力。