Panorama Multi-Focus Fusion

panorama Computational Multi Focus Incoherent
View Benchmarks (1)

Multi-focus panoramic fusion combines images captured at different focal planes and/or different spatial positions to produce an all-in-focus image with extended depth of field and wide field of view. Focus stacking selects the sharpest regions from each focal plane using local contrast measures, then blends them via Laplacian pyramid fusion or wavelet-based methods. Panoramic stitching aligns overlapping images using feature matching (SIFT/SURF) and blends seams. Primary challenges include parallax at scene edges and focus measure ambiguity in low-texture regions.

Forward Model

Defocus Stack

Noise Model

Gaussian

Default Solver

laplacian pyramid fusion

Sensor

CMOS

Forward-Model Signal Chain

Each primitive represents a physical operation in the measurement process. Arrows show signal flow left to right.

C PSF_focus Depth-Dependent PSF Sigma f Focus Stack Sum D g, η₁ Camera
Spec Notation

C(PSF_focus) → Σ_f → D(g, η₁)

Benchmark Variants & Leaderboards

Panorama

Panorama Multi-Focus Fusion

Full Benchmark Page →
Contribute Dataset Compete
Spec Notation

C(PSF_focus) → Σ_f → D(g, η₁)

Standard Leaderboard (Top 10)

# Method Score PSNR (dB) SSIM Trust Source
🥇 PanoFormer 0.808 35.0 0.950 ✓ Certified Image stitching transformer, 2024
🥈 UDIS 0.760 33.0 0.920 ✓ Certified Nie et al., ICCV 2021
🥉 APAP 0.667 29.5 0.850 ✓ Certified Zaragoza et al., CVPR 2013
4 SIFT-RANSAC 0.553 26.0 0.740 ✓ Certified Lowe, IJCV 2004
Mismatch Parameters (3) click to expand
Name Symbol Description Nominal Perturbed
focus_step Δf Focus step error (μm) 0 2.0
registration Δr Inter-frame registration error (pixels) 0 0.5
exposure_variation ΔE Exposure variation (%) 0 3.0

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.

G1 — Forward Model Accuracy How well does the mathematical model match reality?

Model: defocus stack — Mismatch modes: parallax error, registration error, exposure variation, ghost from motion

G2 — Noise Characterization Is the noise model correctly specified?

Noise: gaussian — Typical SNR: 25.0–45.0 dB

G3 — Calibration Quality Are instrument parameters accurately measured?

Requires: focal distances, camera intrinsics, overlap registration, vignetting correction

Modality Deep Dive

Principle

Panoramic multi-focus fusion captures multiple images of the same wide scene at different focal distances and combines them to produce a single all-in-focus panorama with extended depth of field. Image stitching aligns overlapping frames using feature matching and homography estimation, while focus fusion selects the sharpest pixels from each focal plane.

How to Build the System

Mount a camera on a motorized panoramic head (nodal point rotation). For each pan/tilt position, capture a focus stack (3-10 images at different focus distances). Use a medium-aperture setting (f/5.6-f/8) for each frame. Stitch overlapping views (30 % horizontal overlap) and fuse focus stacks per view tile. Calibrate the panoramic head to rotate around the lens entrance pupil to minimize parallax.

Common Reconstruction Algorithms

  • Laplacian pyramid focus fusion (weighted blending by local contrast)
  • SIFT/SURF feature matching + RANSAC homography estimation
  • Multi-band blending (Burt-Adelson) for seamless stitching
  • Exposure fusion (Mertens et al.) for HDR panoramas
  • Deep-learning focus stacking (DFDF, DeepFocus)

Common Mistakes

  • Parallax errors from rotation not centered on the lens entrance pupil
  • Ghosting from moving objects between sequential captures
  • Color inconsistency between overlapping tiles due to auto-exposure variation
  • Incomplete focus coverage leaving blurry regions in the final panorama
  • Stitching artifacts at seam lines visible in the final output

How to Avoid Mistakes

  • Use a calibrated panoramic head; verify no-parallax point for the specific lens
  • Mask out or blend moving objects; capture quickly or use simultaneous multi-camera rigs
  • Lock exposure, white balance, and focus (manual mode) across all tiles
  • Plan focus distances to cover the entire depth range of the scene
  • Use multi-band blending and choose seam lines in textureless regions

Forward-Model Mismatch Cases

  • The widefield fallback applies Gaussian blur to a single image, but panoramic imaging involves geometric projection (cylindrical, spherical, or equirectangular) of the scene onto a wide field of view — the projection geometry is absent
  • Panorama multi-focus fusion requires modeling focus variation across the wide FOV and stitching multiple exposures — the widefield single-frame model cannot capture the spatially varying focus or overlap regions

How to Correct the Mismatch

  • Use the panorama operator that models the geometric projection (cylindrical or spherical warping) and focus-dependent blur across the wide field of view
  • Reconstruct using image stitching with homography estimation, exposure fusion, and spatially varying deblurring that account for the correct projection geometry

Experimental Setup

Image Size

4096x2048 (equirectangular)

Focus Planes

6

Overlap Percent

30

Fusion

Laplacian pyramid / wavelet fusion

Application

all-in-focus / extended depth of field

Signal Chain Diagram

Experimental setup diagram for Panorama Multi-Focus Fusion

Key References

  • Burt & Adelson, 'The Laplacian Pyramid as a Compact Image Code', IEEE Trans. Commun. 31, 532-540 (1983)

Canonical Datasets

  • Lytro multi-focus test set

Related Modalities

Light Field Integral

Benchmark Pages

Panorama