# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 8 Conclusions  
Decolorization methods play a crucial role in image processing by transforming color images into grayscale representations, allowing for easier analysis and interpretationof visual information. The effectiveness of these methods can significantly influence the outcomes of subsequent image-processing tasks, such as edge detection and object segmentation. The results demonstrate that methods like GD and DL are particularly effective for edge detection, yielding superior outcomes. Hausdorff distance, used for evaluation of edge detection, for DL is 3.894 and for GD is 3.941. For comparison, the worst result is more than ten times higher, 49.250 for Rec. 709. Properties important for edge detection were recognized by GRR metric which rated DL and GD as the best decolorization methods. The CDF method is best for object segmentation tasks. IoU, used for the evaluation of object segmentation, for CDF is 0.9251. For comparison, the worst result is 0.7063 for Rec. 709. Properties important for object segmentation were recognized by NRMS metric, which rated CDF as the best decolorization method. Images with low contrast produced the weakest results both for object segmentation and edge detection, emphasizing the importance of contrast in image content for successful decolorization and subsequent tasks. However, the FSIM metric did not align with accuracy in these tasks, suggesting that it may not be suitable for evaluating decolorization when further analysis is required. It is expected because FSIM evaluates decolorization methods taking into account the properties of the human visual system.  
Looking ahead, future research should encompass a broader range of images, a more extensive array of decolorization methods, such as the latest deep learning methods and methods based on image entropy, and additional evaluation metrics, such as E-score and TIA to gain deeper insights into their performance. Furthermore, integrating other tasks, such as image compression and applications in medical imaging, will enhance the understanding of how decolorization techniques can be optimized for specific practical applications. Such investigations will contribute to refining image processing methodologies and improving their applicability in various domains.  
Conceptualization, I.Z. and S.G.; methodology, I.Z. and S.G.; software, I.S. and D.M.; validation, I.Z., I.S., D.M. and S.G.; formal analysis, I.Z., I.S. and D.M.; investigation, I.Z., I.S., D.M. and S.G.; resources, S.G.; data curation, I.S. and D.M.; writing--original draft preparation, I.Z.; writing--review and editing, I.Z. and S.G.; visualization, I.S. and D.M.; supervision, S.G.; project administration, I.Z. and S.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Croatian Science Foundation (HRZZ) under the project number IP-2022-10-3029.  
Not applicable.  
The images used for image decolorization can be found at [[https://cadik.posvete.cz/color_to_gray_evaluation/cadik08](https://cadik.posvete.cz/color_to_gray_evaluation/cadik08) perceptualEvaluation-images.zip]([https://cadik.posvete.cz/color_to_gray_evaluation/cadik08](https://cadik.posvete.cz/color_to_gray_evaluation/cadik08) perceptualEvaluation-images.zip), accessed on 20 February 2024. The neural network model of deep learning method can be found at [[https://github.com/caibolun/DeepDecolor](https://github.com/caibolun/DeepDecolor)]([https://github.com/caibolun/DeepDecolor](https://github.com/caibolun/DeepDecolor)), accessed on 15 April 2024.  
The authors declare no conflicts of interest.