Bachelor's thesis
Topic: Nonlinear filtering for large 3D image data (Bilateral filter, Non-local means filter).
- 2D Bilateral filter (naive implementation) ✔
- Parallel processing ✔
- Fast 2D Bilateral filter (custom optimizations) ✔
- 2D Non-local means filter ✔
- Fast 2D Non-local means filter (integral image) ✔
- CLI for 2D ✔
- rendering 3D volumetric data ✔
- 3D Bilateral filter ✔
- 3D Non-local means filter ✔
- CLI for 3D ✔
- Piecewise processing for large 3D volumetric data
- Optimizations via parallel processing using SIMD
Image:
- House 620x620 (GrayScale) source
Volumetric data:
| Parameter | Value |
|---|---|
| Space sigma | 6 |
| Range sigma | 25.5 |
| Filter | Windows [s] | Ubuntu [s] |
|---|---|---|
| Bilateral (1 thread) | ~ 0.56 | ~ 0.51 |
| Bilateral | ~ 0.16 | ~ 0.15 |
| OpenCV | ~ 0.30 | ~ 0.32 |
| SimpleITK | ~ 1.04 | ~ 0.70 |
| Itk | ~ 0.71 | ~ 0.78 |
Noisy vs Bilateral filter
| Parameter | Value |
|---|---|
| h | 15 |
| Patch size | 3x3 |
| Window size | 21x21 |
| Samples | 500 |
| Filter | Windows [s] | Ubuntu [s] |
|---|---|---|
| Non-local means (1 thread) | ~ 8.0 | ~ 6.26 |
| Non-local means (7 threads) | ~ 2.25 | ~ 1.83 |
| Non-local means sampled (7 threads) | ~ 1.58 | ~ 1.4 |
| Fast Non-local means (1 thread) | ~ 3.36 | ~ 3.15 |
| Fast Non-local means (7 threads) | ~ 2.1 | ~ 2.2 |
| Fast Non-local means sampled (7 threads) | ~ 1.5 | ~ 2 |
| OpenCV | ~ 0.9 | ~ 0.4 |
Note: Fast Non-local means filter complexity is independent to patch size due to integral image optimizations
Noisy vs Non-local means filter
Non-local means filter vs OpenCV Non-local means filter
Sampled Non-local means filter vs OpenCV Non-local means filter
3D rendering of noisy volumetric data using ray casting
| Parameter | Value |
|---|---|
| Domain sigma | 5 |
| Range sigma | 20 |
| Filter | Windows | Ubuntu |
|---|---|---|
| Fast Bilateral (1 thread) | ~ 1.3 min | ~ 1.6 min |
| Fast Bilateral (7 threads) | ~ 25 sec | ~ 27 sec |
| Simple Itk (multi-threaded) | ~ 14.9 min | ~ 9.2 min |
| Itk (multi-threaded) | ~ 12 min | ~ 7.75 min |
Fast bilateral filter
Note: There are no differences discernible to the eye between measured filters.
| Parameter | Value |
|---|---|
| h | 20 |
| Patch size | 3x3x3 |
| Window size | 15x15x15 |
| Samples | 500 |
| Filter | Windows | Ubuntu |
|---|---|---|
| Non-local means sampled (7 threads) | ~ 10.6 min | ~ 10.5 min |
| Fast Non-local means sampled (1 thread) | ~ 4.0 min | ~ 4.2 min |
| Fast Non-local means sampled (7 threads) | ~ 1.4 min | ~ 1.5 min |
| scikit (1 thread) | ~ 7 min | ~ 4.9 min |
Note: Fast Non-local means filter (as well as scikit impl.) complexity is independent to patch size due to integral image optimizations
Non-local means sampled filter
scikit Non-local means filter
| Tool | Windows 10 | Ubuntu 20.04 (WSL 2) |
|---|---|---|
| .NET runtime | 6.0.3 | 6.0.3 |
| Python | 3.10.2 [MSC v.1929 64 bit (AMD64)] | 3.8.10 [GCC 9.4.0] |
| OpenCV | 4.5.5 | 4.5.5 |
| Itk | 5.3.0 | 5.2.1 |
| SimpleITK | 2.2.0rc2.post35 | 2.1.1 |
| scikit image | 0.19.2 | 0.19.2 |
Remainder:
-
Test script (test.ps1) requires 2 powershell scripts (cmp-img.ps1 and join-img.ps1 for Windows only) for full experience.
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For comparing filters with python alternatives, it's required to install dependencies:
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