Disclaimer: The software provided in this repository was developed without the use of generative AI. Generative AI may only be used to verify grammatical correctness and syntax.
The application was originally developed as an R&D project between 2017 and 2019.
The original purpose was to investigate the potential advantages of optimizing the contrast of grayscale images using a normal distribution compared with a uniform distribution. Two parameters - the expectation and standard derivation - allow to the relative luminance and contrast, respectively, to be controlled.
Microkernel Guide&Demo for WPF (SDI), Winforms (MDI/SDI/TDI) and Console processes
- Overview
- Hierarchy of modules
- Navigation by using a DI container
- Closures propagation by using a pipeline and event aggregator
- Linking a transient presenter and a view
- Partial mocks substitution via a DI container to test the internal infrastructure
- Reference a microkernel from a presentation to move a domain between processes
- Managing Grayscale Images
- Benchmarks
- NuGet
Fig. 1 - The main view and transient/signleton views are displayed as tabs. The opened affine transformation tab is a transient view. The settings tab is a singleton view. The frame is taken from the "Thomas the Tank Engine" series and processed using the following algorithm chain: Grayscale->Inversion->Laplacian Operator 5x5->Inversion->Shear Rotation 20°->Bicubic Interpolation (0.2, 0.2)->Cyclic Translation (33, 33) (hold) [cpu].
Fig. 2 Hierarchy of modules.
Initially, a group of underexposed images was chosen for experimental puproses.
Fig. 3 - The original underexposed image.
After optimization using a uniform distribution, there is redundancy in bright areas of the relative luminance. However, using a normal distribution it's possible to minimize this effect and achieve better detail distinctiveness.
Fig. 4 - Histogram transformation using a uniform distribution.
Fig. 5 - Histogram transformation using a normal distribution, where µ = 90 and σ = 60.
To determine which image has better contrast, one may use the definition of conditional variance:
where [z1, z2] is an interval of relative luminance.
By splitting the interval [0, 255] to 16 subintervals, we can use the definition above. Since contrast is defined as statistical scattering, conditional variance can be used to measure the level of contrast within each specified interval.
Fig. 6 - Using the definition of conditional variance over 16 intervals of relative luminance.
Thus, one may conclude that a normal distribution may produce better results than a uniform distribution for a group of underexposed images.
ImageProcessing.Microkernel.DIAdapter