## 2 Related Works  
### Color Standardization  
There are numerous categories of color calibration problems, with two fundamental groups pertaining to either the use of different devices within a series of photos or variations in the lighting conditions affecting the photographed subjects. Hardware color compensation can be achieved by employing camera sensors that automatically adjust settings based on the lighting of the scene being captured. The alternative approach involves numerical processing of photos using various strategies that ultimately aim to achieve consistent color tones across multiple images. To accomplish this, calibration charts are initially employed, which correlate and map the individual pixels of captured photos with the standardized values of the corresponding color patches on the chart [2; 3]. A standard color calibration chart serves as a reference tool in photography, film creation, video recording, or graphic design, ensuring accurate and consistent color reproduction in images. Typically, it comprises a grid of colored patches with known and standardized colors. These charts are applied in calibrating equipment and adjusting during post-processing, thereby ensuring consistency in color representation across various devices and conditions. Commercially, several calibration charts are available, ranging from those with six to 100 color patches. However, the 24-color patch calibration chart, often referred to by its original name as the Macbeth ColorChecker, has found widespread use in numerous applications [4]. Additionally, the development of customized calibration charts plays an important role in standardizing image colors. Color standardization plays an important role in many fields of science and technology, including medical diagnostics, bioanalysis, agriculture, analysis of evidence in forensic sciences, analytical chemistry, etc. In agricultural image processing, the focus lies in linking alterations in the color of images, in designated areas of interest, with particular quality attributes such as plant health, crop stress, and maturity level. In [5], a technique was proposed to calibrate digital images, ensuring their uniformity and improving phenological comparisons in agriculture. The color transformation of the images was achieved using a standard 24-color calibration chart. Calibration delivers typical and expected results by adjusting the RGB values of the color patches in the input image to standard RGB values. In [6], a digital shade-matching device was developed for determining tooth color, as accurate shade assessment is crucial in dental restoration procedures. Image recordings made with an intraoral camera served as the basis for the calculations and transformations. The support vector machine (SVM) algorithm and the CIEL*a*b* color space were used to evaluate tooth color. The color correction was based on the calculation of the Euclidean distance between the shades. The authors created a dedicated template, distinct from the typical one with a broad spectrum of colors, specifically adapted to the range of tooth color variations addressed in the study. The next proposition concerns improving the accuracy of color reproduction in digital cameras and smartphones. The first method uses white balance correction followed by a color space transformation. The second approach, applicable to images processed offline, is based on performing full color balance instead of white balance and requires the use of machine learning to predict the color balance matrix [7]. The review paper [8] discusses the use of various histopathological image recognition algorithms. The differences in color in histopathology result from the use of various equipment or tissue staining techniques. Typically, color normalization involves transferring the average color of the target image to the source image and separating the stain present in the source image.