Fiber bundles have become widely adopted for use in endoscopy, live-organism imaging, and other imaging applications. An inherent consequence of imaging with these bundles is the introduction of a honeycomb-like artifact that arises from the inter-fiber spacing, which obscures features of objects in the image. This artifact subsequently limits applicability and can make interpretation of the image-based data difficult. This work presents a method to reduce this artifact by on-axis rotation of the fiber bundle. Fiber bundle images were first low-pass and median filtered to improve image quality. Consecutive filtered images with rotated samples were then co-registered and averaged to generate a final, reconstructed image. The results demonstrate removal of the artifacts, in addition to increased signal contrast and signal-to-noise ratio. This approach combines digital filtering and spatial resampling to reconstruct higher-quality images, enhancing the utility of images acquired using fiber 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.
more » « less- NSF-PAR ID:
- 10397385
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Optics Letters
- Volume:
- 48
- Issue:
- 5
- ISSN:
- 0146-9592; OPLEDP
- Page Range / eLocation ID:
- Article No. 1088
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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