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 con-fined 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 imag-ing (CI) can be combined with spectral coding to overcome these two fundamental limitations and improve spatial reso-lution 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 bun-dle 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 5x smaller than individ-ual fiber cores were resolved, whereas conventional imaging could only resolve features at least 1.5x 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.(c) 2023 Optica Publishing Group

Automated summary

Fiber optic bundles are commonly used in narrow-diameter instruments to acquire images from confined spaces. However, the current imaging method has limitations in spatial resolution due to the information restriction in fiber cores and the loss of information in the cladding region. In this study, computational imaging (CI) combined with spectral coding was proposed to overcome these limitations and improve spatial resolution in fiber bundle imaging.