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
- Microkernel
- Application
- Integration tests
- Managing Grayscale Images
- Created With
- Benchmarks
- NuGet
The application consists of a desktop window with a main viewport implemented as a PictureBox where the sequentially transformed images are rendered. The menu on the top allows users to generate views, which are appended to the bottom. The toolbar below the menu allows to undo or redo transformations, restore the originally loaded image, and capture a custom area within the viewport. Two trackbars apply rotation or scaling transformations without rendering an image.
Each generated view produces a tab below the viewport, allowing users to apply algorithms or manage related parameters.
Fig. 1 - The main view and transient/singleton 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].
The project is built around two major namespaces: Microkernel and App . As discussed below, this hierarchy allows an application to scale vertically, from views to presenters to service/provider/factory components, and horizontally, by adding subsystems within these categories. Decoupling the UI does not limit the application to static forms; it also allows to apply XML/XAML-based interfaces or console applications to use the same set of event handlers and domain modules. Additionally, as demonstrated in this application, the hierarchy supports comprehensive integration testing of any component, including the verification of incoming arguments and function calls.
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.
Serilog, Microsoft.Extensions.Logging
Microsoft.Extensions.Configuration