feat(n4): GPU-accelerated N4 bias field correction#109
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The new function detects the galvo shift using the normalized mutual information between the galvo return and the image.
Scripts to perform and apply manual correction to 2.5D volume
… detection/fix to xyzcorr.py
…and refactor volume loading to support JSON configuration
2.5D/3D reconstruction pipelines bug fixes
Copilot recommendation Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Charles Poirier <41654474+CHrlS98@users.noreply.github.com>
Co-authored-by: Charles Poirier <41654474+CHrlS98@users.noreply.github.com>
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Superseded by #116 — recreated on the proper base |
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PR #109 — N4 Bias Field Correction (CPU + GPU)
Adds N4 bias-field correction with CPU/GPU backends, replacing the previous z-intensity normalization.
Library
linumpy/intensity/bias_field.py— high-level APIlinumpy/intensity/normalization.py— extended for N4 backend, plusfix(normalize): zero agarose per slice while preserving inter-slice brightness(per-slice subtraction of the agarose-median floor pins background voxels to exactly 0 in the normalized output, restoring pre-n4 visualization behavior; global divisor preserves the 2:1 inter-section brightness invariant)linumpy/gpu/{bias_field,bspline,n4}.py— GPU kernels (CuPy)linumpy/gpu/normalization.py(superseded)Scripts
scripts/linum_correct_bias_field.py— replaceslinum_normalize_z_intensityscripts/diagnostics/linum_benchmark_n4_gpu.pyscripts/diagnostics/linum_n4_gpu_visual_compare.pyTests
linumpy/tests/test_bias_field*.py,test_gpu_{bspline,n4}.pylinumpy/tests/test_n4_gpu_{equivalency,perf}.pytest_intensity_normalization.pywithtest_normalize_volume_agarose_floor_at_zeroWorkflow integration
workflows/reconst_3d/soct_3d_reconst.nf: addscorrect_bias_fieldprocessworkflows/reconst_3d/nextflow.config: N4 parametersBuild
pyproject.toml: entry-point swap (linum_normalize_z_intensity→linum_correct_bias_field),scipy-stubs/networkx-stubsdev deps, lint exemptions forlinumpy/gpu/*and N4 diagnostic scripts