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

Abstract

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.

Fiberscopic Pattern Removal for Optimal Coverage in 3D Bladder Reconstructions of Fiberscope Cystoscopy Videos

“…Less direct approaches also exist, where the primary aim is to improve image resolution and the fiberscope artifacts may be consequently removed. These methods combine multiple images taken at different positions into a single higher-resolution image [21][22][23] or use computational imaging and spectral coding to extract intra-and inter-fiber data that are combined into a higher-resolution image [24]. However, many of these methods do not fully remove the fiberscope artifacts, and all require additional hardware or careful manipulation of the cystoscope, both of which are difficult to implement in clinical cystoscopies.…”

Fiberscopic pattern removal for optimal coverage in 3D bladder reconstructions of fiberscope cystoscopy videos

“…Less direct approaches also exist, where the primary aim is to improve image resolution and the fiberscope artifacts may be consequently removed. These methods combine multiple images taken at different positions into a single higher-resolution image 20 – 22 or use computational imaging and spectral coding to extract intra- and interfiber data that are combined into a higher-resolution image 23 . However, many of these methods do not fully remove the fiberscope artifacts, and all require additional hardware or careful manipulation of the cystoscope, both of which are difficult to implement in clinical cystoscopies.…”