Fiber Bundle Endoscopy
Fiber bundle endoscopy transmits images through a coherent fiber bundle of 10,000-50,000 individual optical fibers. Each fiber core acts as a spatial sample, producing a honeycomb pattern. Image quality is limited by inter-core spacing (pixelation), inter-core coupling (crosstalk), and core-to-core transmission variation. White-light or narrow-band illumination is delivered through the bundle or alongside it. Reconstruction involves core localization, transmission calibration, interpolation to a regular grid, and denoising.
Fiber Sampling
Poisson Gaussian
tv fista
CMOS
Forward-Model Signal Chain
Each primitive represents a physical operation in the measurement process. Arrows show signal flow left to right.
C(PSF_fiber) → D(g, η₁)
Benchmark Variants & Leaderboards
Endoscopy
Fiber Bundle Endoscopy
C(PSF_fiber) → D(g, η₁)
Standard Leaderboard (Top 10)
| # | Method | Score | PSNR (dB) | SSIM | Trust | Source |
|---|---|---|---|---|---|---|
| 🥇 | DiffEndo | 0.890 | 39.7 | 0.957 | ✓ Certified | Gao et al. 2024 |
| 🥈 | PhysEndo | 0.863 | 38.4 | 0.947 | ✓ Certified | Chen et al. 2024 |
| 🥉 | SwinEndo | 0.840 | 37.3 | 0.937 | ✓ Certified | Li et al. 2023 |
| 4 | TransEndo | 0.809 | 35.9 | 0.921 | ✓ Certified | Wang et al. 2022 |
| 5 | EndoSLAM-Net | 0.758 | 33.8 | 0.889 | ✓ Certified | Ozyoruk et al. 2021 |
| 6 | DnCNN-Endo | 0.701 | 31.4 | 0.855 | ✓ Certified | Zhang et al. 2017 |
| 7 | BM3D-Endo | 0.638 | 28.9 | 0.812 | ✓ Certified | Dabov et al. 2007 |
| 8 | CLAHE-Endo | 0.578 | 26.5 | 0.772 | ✓ Certified | Zuiderveld 1994 |
| 9 | Histogram-Eq | 0.521 | 24.1 | 0.738 | ✓ Certified | Gonzalez & Woods 2002 |
Mismatch Parameters (3) click to expand
| Name | Symbol | Description | Nominal | Perturbed |
|---|---|---|---|---|
| fiber_coupling | Δη_f | Fiber coupling efficiency error (%) | 0 | 5.0 |
| core_spacing | Δd | Core spacing error (μm) | 0 | 0.5 |
| bending_loss | Δα_b | Bending loss error (dB) | 0 | 0.3 |
Reconstruction Triad Diagnostics
The three diagnostic gates (G1, G2, G3) characterize how reconstruction quality degrades under different error sources. Each bar shows the relative attribution.
Model: fiber sampling — Mismatch modes: core crosstalk, fixed pattern noise, bending loss, specular reflection
Noise: poisson gaussian — Typical SNR: 20.0–38.0 dB
Requires: core map, transmission calibration, distortion coefficients, flat field
Modality Deep Dive
Principle
Fiber-bundle endoscopy transmits an image through a flexible coherent fiber bundle (10,000-100,000 individual fiber cores) to visualize internal body cavities. Each fiber core acts as a single pixel, transmitting light from the distal end to the proximal end where a camera captures the image. The hexagonal fiber packing imposes a fixed pixelation pattern (comb/honeycomb structure) on the image.
How to Build the System
A medical endoscope has a flexible insertion tube containing the coherent fiber bundle (or a distal CMOS chip for video endoscopes), illumination fibers, working channels, and air/water channels. Light source: LED or Xenon lamp transmitted through illumination fibers. For fiber-bundle type: attach a high-resolution camera and relay lens at the proximal end. Calibrate fiber core positions and individual fiber transmission for computational image improvement.
Common Reconstruction Algorithms
- Fiber core mapping and interpolation (honeycomb artifact removal)
- Deep-learning super-resolution for fiber-bundle images
- Structure-from-motion for endoscopic 3-D reconstruction
- Defogging / dehazing for underwater or smoke-obscured endoscopy
- Real-time mosaicking for extended field-of-view endoscopy
Common Mistakes
- Honeycomb pattern artifact from fiber core spacing not removed
- Broken fibers (dark spots) accumulating over time and degrading image quality
- Specular reflections (glare) from wet tissue surfaces saturating the image
- Insufficient illumination causing noisy images in deep body cavities
- Image distortion from fiber bundle bending not corrected
How to Avoid Mistakes
- Apply fiber core interpolation or deep-learning super-resolution in post-processing
- Replace fiber bundles when broken fiber percentage exceeds acceptable threshold
- Use polarization filtering or computational specular removal algorithms
- Use bright LED sources and adjust exposure/gain for adequate signal
- Calibrate and correct for bending-dependent distortion using test patterns
Forward-Model Mismatch Cases
- The widefield fallback produces a (64,64) image, but fiber-bundle endoscopy transmits images through discrete fiber cores creating a hexagonal pixelation pattern — output shape (n_fibers,) is a 1D vector of per-core intensities
- The fiber bundle imposes a fixed sampling grid (honeycomb structure) with inter-core crosstalk and dead fibers — the widefield continuous Gaussian blur has no relationship to the discrete fiber sampling and transmission physics
How to Correct the Mismatch
- Use the endoscopy operator that models per-fiber-core sampling: each of the ~10,000-100,000 cores transmits a point sample from the distal end to the proximal camera, with known core positions and transmission coefficients
- Reconstruct using fiber-core interpolation, honeycomb artifact removal, or deep-learning super-resolution that account for the known fiber bundle geometry and per-core response
Experimental Setup
Olympus GIF-H290Z / Karl Storz IMAGE1 S
30000
1920x1080 (HD output)
60
140
3.7
400-700 (white light)
Kvasir, CVC-ClinicDB, HyperKvasir
Signal Chain Diagram
Key References
- Lee & Bhatt, 'Fiber bundle endoscopy advances', J. Biophotonics 12, e201900004 (2019)
Canonical Datasets
- Kvasir-SEG (polyp segmentation)
- CVC-ClinicDB (colonoscopy)
- HyperKvasir (multi-class GI dataset)