Quantitative Analysis of Blurry Color Image Fusion Techniques using Color Transform
DOI:
https://doi.org/10.23851/mjs.v34i3.1373Keywords:
Motion Blur, Colour Space Transformation, Fusion Techniques, Quality CriteriaAbstract
This work focuses on fused color images resulting from motion blur (left and right) with a blur block size of 11 pixels. The color conversion process was performed from RGB color space (Red, Green, Blue) to HSV (Hue, Saturation, Value), L*a*b*, and Ycbcr (Luminance, Chrominance) color space. The traditional (addition, multiplication) and proposed fusion techniques (absolute real standard deviation) were used for this purpose. The data was examined by quality criteria with reference (Mutual Information, Correlation Coefficient, Structural Content, Normalized Cross Correlation) and without reference (Blind Reference less Image Spatial Quality Evaluator, Naturalness Image Quality Evaluator, and Perception-based Image Quality Evaluator). In results and depending on the criteria, the best fusion method is the proposed real standard deviation.
Received: 17/04/2023
Revised: 21/05/2023
Accepted: 28/05/2023
Downloads
References
Abbas, H.K., et al., New algorithms to enhanced fused images from auto-focus images. Baghdad Sci. J, 2021. 10(18): p. 1.
Abbas, H.K., et al., Adopting Image Integration Techniques to Simulate Satellite Images. Iraqi Journal of Science, 2020: p. 3445-3455.
Salau, A.O., S. Jain, and J. NnennaEneh, A review of various image fusion types and transform. Indonesian Journal of Electrical Engineering and Computer Science, 2021. 24(3): p. 1515-1522.
Wang, X., et al., A unified multiscale learning framework for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 2022. 60: p. 1-19.
Hou, Z., et al., A Remote Sensing Image Fusion Method Combining Low-Level Visual Features and Parameter-Adaptive Dual-Channel Pulse-Coupled Neural Network. Remote Sensing, 2023. 15(2): p. 344.
Zhou, W., et al., Improved estimation of motion blur parameters for restoration from a single image. Plos one, 2020. 15(9): p. e0238259.
Li, S., et al., Pixel-level image fusion: A survey of the state of the art. Information Fusion, 2017. 33: p. 100-112.
Al-Mokhtar, Z.T., F.N. Ibraheem, and H.F. Al-Layla, A Review of Digital Image Fusion and its Application. Al-Rafidain Engineering Journal (AREJ), 2021. 26(2): p. 309-322.
Askari Javaran, T. and H. Hassanpour, Using a Blur Metric to Estimate Linear Motion Blur Parameters. Computational and Mathematical Methods in Medicine, 2021. 2021: p. 1-8.
Al-Jasim, A.A.N., T.A. Naji, and A.H. Shaban, The Effect of Using the Different Satellite Spatial Resolution on the Fusion Technique. Iraqi Journal of Science, 2022: p. 4131-4141.
Perez‐Udell, R.A., A.T. Udell, and S.M. Chang, An automated pipeline for supervised classification of petal color from citizen science photographs. Applications in Plant Sciences, 2023: p. e11505.
Yee, A.L.K., et al., Preliminary analysis of rock mass weathering grade using image analysis of CIELAB color space with the validation of Schmidt hammer: A case study. Physics and Chemistry of the Earth, Parts A/B/C, 2023. 129: p. 103291.
Dwivedi, R. and V.K. Srivastava. An Imperceptible and Robust image watermarking using RDWT and SVD in YCbCr color space. in 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). 2022. IEEE.
Wu, L. and L. Zhao. ISAR Image Registration Based on Normalized Correlation Coefficient. in 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA). 2023. IEEE.
Golestaneh, S.A., S. Dadsetan, and K.M. Kitani. No-reference image quality assessment via transformers, relative ranking, and self-consistency. in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2022.
Zhou, M., et al. Mutual information-driven pan-sharpening. in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.
Lu, W., et al. Blind Surveillance Image Quality Assessment via Deep Neural Network Combined with the Visual Saliency. in Artificial Intelligence: Second CAAI International Conference, CICAI 2022, Beijing, China, August 27-28, 2022, Revised Selected Papers, Part II. 2023. Springer.
Gwon, G.-H., et al., CNN-based Image Quality Classification Considering Quality Degradation in Bridge Inspection using an Unmanned Aerial Vehicle. IEEE Access, 2023.
Wu, L., et al., VP-NIQE: An opinion-unaware visual perception natural image quality evaluator. Neurocomputing, 2021. 463: p. 17-28.
Downloads
Key Dates
Published
Issue
Section
License
Copyright (c) 2023 Al-Mustansiriyah Journal of Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Articles accepted for publication in Al-Mustansiriyah Journal of Science (MJS) are protected under the Creative Commons Attribution 4.0 International License (CC BY-NC). Authors of accepted articles are requested to sign a copyright release form prior to their article being published. All authors must agree to the submission, sign copyright release forms, and agree to be included in any correspondence between MJS and the authors before submitting a work to MJS. For personal or educational use, permission is given without charge to print or create digital copies of all or portions of a MJS article. However, copies must not be produced or distributed for monetary gain. It is necessary to respect the copyright of any parts of this work that are not owned by MJS.
How to Cite
