# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 7 Discussion  
The evaluation results of the seven decolorization methods presented in [6] demonstrate that the GD method [21] is among the top-performing grayscale conversion approaches. This aligns with our findings, which confirm the GD method as a highly effective decolorization technique. Consistent with the observations in [21], our research reveals that no decolorization method consistently delivers the best results across all input images, highlighting a key limitation: the lack of robustness in current methods when applied to diverse inputs.  
Our results further indicate that edge detection accuracy depends on both the test image and the decolorization method. The best edge detection performance was observed for images decolorized using the GD and DL methods. The poorest edge detection results for all decolorization methods were obtained for test images with the lowest contrast between objects and the background. Notably, the GD decolorization method performs well even for these challenging images, providing a strong foundation for subsequent edge detection tasks.  
The GD algorithm stands out from other methods because it combines contrast enhancement with color-to-grayscale conversion. This contrast enhancement plays a key role in its excellent performance in edge detection tasks, as it helps to clearly highlight important image features needed for accurate edge detection.  
GRR effectively captures the strengths of the GD and DL algorithms, particularly for images containing colors with nearly identical luminance values (isoluminant regions). These colors often appear indistinguishable in grayscale, which can cause problems in tasks such as edge detection, object recognition, or segmentation. By preserving contrast even in isoluminant regions, the GD and DL algorithms mitigate these challenges, improving overall analysis and performance.  
In [9], the CDF decolorization method is compared to several classical algorithms and two modern deep learning-based methods from the literature. The results show that the CDF method outperforms other methods in retaining image details and contrast. Compared with other benchmark approaches, the CDF method consistently delivers visually appealing results. In our evaluation, the CDF method, assessed using the NRMS metric achieves the best performance across nearly all images. The NRMS metric, which measures the average intensity difference between decolorized and input images, highlights the CDF method's ability to preserve contrast. This capability directly translates into superior performance in object segmentation tasks, where the CDF method achieves the highest average IoU and DSC scores. These results demonstrate that contrast-preserving decolorization methods like CDF are highly effective as a preprocessing step for object segmentation tasks.  
Basic decolorization methods, based on the properties of the human visual system, outperform other methods when evaluated with FSIM that is also based on the properties of the human visual system. FSIM does not estimate the accuracy of object segmentation and edge detection and FSIM should not be used to evaluate decolorization if subsequent tasks are related to object segmentation and edge detection.  
The DL method proposed in [30] effectively preserves both fine details and overall image saliency, demonstrating superior edge preservation and saliency mapping compared to baseline methods. This method is evaluated using CCR, which measures how well the grayscale image retains color contrast perceptible to the human eye. The results presented in [30] highlight problems with isoluminant colors and artificial images. However, our findings do not fully support these results when evaluating the DL method using GRR. In our case, the DL algorithm performs particularly well for images containing isoluminant colors and generated images, as the GRR effectively captures the strengths of the DL method in these scenarios.