Computational imaging with spectral coding increases the spatial resolution of fiber optic bundles.

Fiber optic bundles are used in narrow-diameter medical and industrial instruments for acquiring images from confined locations. Images transmitted through these bundles contain only one pixel of information per fiber core and fail to capture information from the cladding region between cores. Both factors limit the spatial resolution attainable with fiber bundles. We show here that computational imaging (CI) can be combined with spectral coding to overcome these two fundamental limitations and improve spatial resolution in fiber bundle imaging. By acquiring multiple images of a scene with a high-resolution mask pattern imposed, up to 17 pixels of information can be recovered from each fiber core. A dispersive element at the distal end of the bundle imparts a wavelength-dependent lateral shift on light from the object. This enables light that would otherwise be lost at the inter-fiber cladding to be transmitted through adjacent fiber cores. We experimentally demonstrate this approach using synthetic and real objects. Using CI with spectral coding, object features 5× smaller than individual fiber cores were resolved, whereas conventional imaging could only resolve features at least 1.5× larger than each core. In summary, CI combined with spectral coding provides an approach for overcoming the two fundamental limitations of fiber optic bundle imaging.

Computational endoscopy-a framework for improving spatial resolution in fiber bundle imaging.

This Letter presents a framework for computational imaging (CI) in fiber-bundle-based endoscopy systems. Multiple observations are acquired of objects spatially modulated with different random binary masks. Sparse-recovery algorithms then reconstruct images with more resolved pixels than individual fibers in the bundle. Object details lying within the diameter of single fibers are resolved, allowing images with 41,663 resolvable points to be generated through a bundle with 2,420 fibers. Computational fiber bundle imaging of micro- and macro-scale objects is demonstrated using fluorescent standards and biological tissues, including in vivo imaging of a human fingertip. In each case, CI recovers details that conventional endoscopy does not provide.

Computational imaging with a highly parallel image-plane-coded architecture: challenges and solutions.

This paper investigates a highly parallel extension of the single-pixel camera based on a focal plane array. It discusses the practical challenges that arise when implementing such an architecture and demonstrates that system-specific optical effects must be measured and integrated within the system model for accurate image reconstruction. Three different projection lenses were used to evaluate the ability of the system to accommodate varying degrees of optical imperfection. Reconstruction of binary and grayscale objects using system-specific models and Nesterov's proximal gradient method produced images with higher spatial resolution and lower reconstruction error than using either bicubic interpolation or a theoretical system model that assumes ideal optical behavior. The high-quality images produced using relatively few observations suggest that higher throughput imaging may be achieved with such architectures than with conventional single-pixel cameras. The optical design considerations and quantitative performance metrics proposed here may lead to improved image reconstruction for similar highly parallel systems.