# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 6 Results
### Results of Object Segmentation and Edge Detection on Decolorized Images
Object segmentation is performed using Chan-Vese method [34]. The Chan-Vese method is a widely used technique in image processing for object segmentation, particularly suitable for separating objects with smooth boundaries and varying intensities. This method segments objects by minimizing an energy function that considers both the object's interior and exterior regions, making it effective even for objects that do not have clear edges or boundaries. In Python 3.8.5 the scikit-image library [35] offers the \(Chan-Vese\) function for implementing this method, making it accessible to researchers and developers in the field of computer vision. Segmentation accuracy was evaluated using the intersection over union (IoU) metric and Dice similarity coefficient (DSC). Both measures are commonly used for assessing the accuracy of object segmentation, comparing the overlap between the predicted and ground truth regions. It is calculated by dividing the area of overlap (intersection) between the predicted segmentation and the ground truth by the total area covered by both regions (union):  
\[\text{IoU}=\frac{\left|A\cap B\right|}{\left|A\cup B\right|}, \tag{21}\] \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Test Image** & **CDF** & **DL** & **GD** & **LAB** & **Rec. 601** & **Rec. 709** & **Rec. 2020** &
\begin{tabular}{c} **Image Mean** \\ \(\pm\) **SD** \\ \end{tabular} \\ \hline gray\_surface\_fully\_saturated & 0.790 & 0.730 & 0.730 & 0.790 & 0.910 & **0.980** & 0.870 & 0.830 \(\pm\) 0.095 \\ \hline white\_surface\_fully\_saturated & 0.860 & 0.820 & 0.750 & 0.830 & 0.940 & **0.980** & 0.920 & 0.870 \(\pm\) 0.080 \\ \hline black\_surface\_fully\_saturated & 0.880 & 0.020 & 0.770 & 0.880 & 0.950 & **0.990** & 0.930 & 0.770 \(\pm\) 0.340 \\ \hline gray\_surface\_not\_saturated & 0.860 & 0.790 & 0.710 & 0.860 & 0.940 & **0.990** & 0.920 & 0.870 \(\pm\) 0.095 \\ \hline white\_surface\_not\_saturated & 0.950 & 0.900 & 0.880 & 0.940 & 0.980 & **0.990** & 0.970 & 0.940 \(\pm\) 0.041 \\ \hline black\_surface\_not\_saturated & 0.870 & 0.020 & 0.730 & 0.860 & 0.940 & **0.980** & 0.930 & 0.760 \(\pm\) 0.337 \\ \hline \multirow{2}{*}{Method mean \(\pm\) SD} & 0.868 & 0.547 & 0.762 & 0.860 & 0.943 & **0.985** & 0.923 & \\  & \(\pm\) 0.051 & \(\pm\) 0.412 & \(\pm\) 0.061 & \(\pm\) 0.050 & \(\pm\) 0.023 & \(\pm\) **0.005** & \(\pm\) 0.032 & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: FSIM for different test images and different decolorization methods. The best results are shown in bold. Figure 7: Generated test images: (**a–c**) saturated color objects on gray, white, and black background, (**d–f**) desaturated color objects on gray, white, and black background. Analysis of the segmentation results shows that accuracy depends on both the test image and the decolorization method. The best object segmentation results were achieved for images decolorized using the CDF and DL methods, which yielded the highest average IoU (CDF method mean = 0.9251, DL method mean = 0.9012) and DSC (CDF method mean = 0.9548, DL method mean = 0.9362). Regarding specific images, the highest IoU and DSC were achieved for Figure 8e _white_surface_not_saturated_ (IoU image mean = 0.9998, DSC image mean = 0.9999). The lowest IoU results were obtained for Figure 8a _gray_surface_fully_saturated_ (0.6264), which has the lowest contrast between objects and the background. This trend is also evident in Figure 9b _gray_surface_fully_saturated_, where only three objects were detected. To examine how different decolorization methods affect edge detection we applied the Canny edge detector in combination with morphological operations [36]. This combination is effective in achieving cleaner and more accurate segmentation. The Canny edge detection method is a popular technique used to identify edges in an image by highlighting areas of significant intensity changes. It involves several steps, including noise reduction, gradient calculation, non-maximum suppression, and hysteresis thresholding, resulting in a precise edge map.