Large depth-of-field computational imaging with multi-spectral and dual-aperture optics

Large DOF (depth-of-field) with high SNR (signal-noise-ratio) imaging is a crucial technique for applications from security monitoring to medical diagnostics. However, traditional optical design for large DOF requires a reduction in aperture size, and hence with a decrease in light throughput and SNR. In this paper, we report a computational imaging system integrating dual-aperture optics with a physics-informed dual-encoder neural network to realize prominent DOF extension. Boosted by human vision mechanism and optical imaging law, the dual-aperture imaging system is consisted of a small-aperture NIR camera to provide sharp edge and a large-aperture VIS camera to provide faithful color. To solve the imaging inverse problem in NIR-VIS fusion with different apertures, a specific network with parallel double encoders and the multi-scale fusion module is proposed to adaptively extract and learn the useful features, which contributes to preventing color deviation while preserving delicate scene textures. The proposed imaging framework is flexible and can be designed in different protos with varied optical elements for different applications. We provide theory for system design, demonstrate a prototype device, establish a real-scene dataset containing 3000 images, perform elaborate ablation studies and conduct peer comparative experiments. The experimental results demonstrate that our method effectively produces high-fidelity with larger DOF range than input raw images about 3 times. Without complex optical design and strict practical limitations, this novel, intelligent and integratable system is promising for variable vision applications such as smartphone photography, computational measurement, and medical imaging.