Conditional GAN-based deep network for seamless large-FOV imaging by camera array

Due to limited spatial bandwidth, one has to compromise between large field of view and high spatial resolution in both photography and microscopy. This dilemma largely hampers revealing fine details and global structures of the target scene simultaneously. Recently, a mainstream method is formed by utilizing multiple sensors for synchronous acquisition across different sub-FOVs with high resolution and stitching the patches according to the spatial position of the cameras. Various inpainting algorithms have been proposed to eliminate the intensity discontinuities, but conventional optimization methods are prone to misalignment, seaming artifacts or long processing time, and thus unable to achieve dynamic gap elimination. By taking advantage of generative adversarial networks (GANs) on image generation and padding, we propose a conditional GAN-based deep neural network for seamless gap inpainting. Specifically, a short series of displaced images are acquired to characterize the system configuration, under which we generate patch pairs with and without gap for deep network training. After supervised learning, we can achieve seamless inpainting in gap regions. To validate the proposed approach, we apply our approach on real data captured by large-scale imaging systems and demonstrate that the missing information at gaps can be retrieved successfully. We believe the proposed method holds potential for all-round observation in various fields including urban surveillance and systems biology.