پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 49 |
| تعداد شماره‌ها | 1,330 |
| تعداد مقالات | 11,470 |
| تعداد مشاهده مقاله | 24,830,194 |
| تعداد دریافت فایل اصل مقاله | 16,606,992 |
Enhancing the Stability of Noise Removal in Color Images Using Eigenvector Analysis and Weighted Bootstrap Mean | ||
| Journal of Advances in Statistical Learning and Data Analysis | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 24 مهر 1405 اصل مقاله (2.13 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.30473/jaslda.2026.78206.1004 | ||
| نویسنده | ||
| Reza Ghasemi* | ||
| Department of Statistics, Payame Noor University, Tehran, Iran | ||
| چکیده | ||
| Noise removal in color images is challenging because effective denoising must suppress noise while preserving edges, textures, and inter-channel relationships. This paper develops a Weighted Bootstrap Mean (WBM) method based on eigenvector analysis of a local data matrix constructed from color values within each pixel neighborhood. The eigenvector-based analysis, referred to as EIG, serves as the baseline and characterizes local inter-channel correlation, distinguishes smooth regions from edges and textured areas, and adapts smoothing to local image structure. EIG is not a contribution of this work; it is reformulated to provide a self-contained description of the baseline and a direct basis for comparison. The main contribution is WBM, which extends EIG by replacing its single plug-in weighted mean with an ensemble of exponentially weighted bootstrap resamples of the local neighborhood. Samples closer to the local mean receive higher selection probabilities, reducing the influence of outliers and high-amplitude noise while retaining local information. Experiments on the Lenna and Flower images at σ = 10, 20, and 30 show that WBM consistently achieves the lowest MAE among CRF, FWD, and EIG. WBM also provides the highest PSNR in most cases. Compared with EIG, WBM consistently improves MAE and achieves higher PSNR on Flower. On Lenna at σ = 30, however, EIG yields a marginally higher PSNR, indicating that WBM’s principal advantage lies in robustness and detail preservation rather than uniform improvement across metrics. These findings demonstrate the potential of weighted bootstrap aggregation as a robust extension of eigenvector-based local denoising. | ||
تازه های تحقیق | ||
| ||
| کلیدواژه‌ها | ||
| Gaussian noise reduction؛ Eigenvector analysis؛ Weighted bootstrap؛ Color image filtering | ||
| مراجع | ||
|
[1] Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one, Multiscale Modeling & Simulation, 4(2), 490–530 (2005). https://doi.org/10.1137/ 040616024 [2] Buades, A., Coll, B., Morel, J.M.: Non-local means denoising, Image Processing On Line, 1, 208–212 (2011). https://doi.org/10.5201/ipol.2011.bcm_nlm [3] Dabov, K., et al.: Image denoising by sparse 3D transform-domain collaborative filtering, IEEE Transactions on Image Processing, 2007. https://doi.org/10.1109/TIP.2007. 901238 [4] Elad, M., Kawar, B., Vaksman, G.: Image denoising: the deep learning revolution and beyond – a survey paper, SIAM Journal on Imaging Sciences, 16(3), 1594–1654 (2023). https://doi.org/10.1137/23M1545859 [5] Geng, X., Guo, Q., Hui, S., Yang, M., Zhang, C.: Tensor robust PCA with nonconvex and nonlocal regularization, Computer Vision and Image Understanding, 243, 104007 (2024). https://doi.org/10.1016/j.cviu.2024.104007 [6] Guo, Q., et al.: Color image denoising based on vector field analysis, IEEE Transactions on Image Processing, 2019. [7] Latorre-Carmona, P., Mi˜nana, J.-J., Morillas, S.: Colour image denoising by eigenvector analysis of neighbourhood colour samples, Signal, Image and Video Processing, 2019.https: //doi.org/10.1007/s11760-019-01575-5 [8] Li, X.: On modeling interchannel dependency for color image denoising, International Journal of Imaging Systems and Technology, Special issue on applied color image processing, 17, 163–173 (2007).https://doi.org/10.1002/ima.20112 [9] Lukac, R., Plataniotis, K.N.: A taxonomy of color image filtering and enhancement solutions, Advances in Imaging and Electron Physics, 140, 187–264 (2006). https://doi.org/10. 1016/S1076-5670(05)40004-X [10] Morillas, S., Gregori, V., Domingo, J., and Sapena, A.: Fuzzy peer groups for color image filtering, IEEE Transactions on Image Processing, 15(11), 3232–3242 (2006). [11] Morillas, S., Gregori, V. (2011). Robustifying Vector Median Filter. Sensors, 11(8), 8115-8126. https://doi.org/10.3390/s110808115 [12] Schulte, S., Huysmans, B., Pizurica, A., Kerre, E.E., Philips, W.: A new fuzzy-based wavelet shrinkage image denoising technique, in Proceedings of Advanced Concepts for Intelligent Vision Systems (ACIVS 2006), Lecture Notes in Computer Science, 4179, 12–23 (2006). https://doi.org/10.1007/11864349_2 [13] Schulte, S., De Witte, V., Kerre, E.E.: A fuzzy noise reduction method for colour images, IEEE Transactions on Image Processing, 16, 1425–1436 (2007). https://doi.org/10.1109/ TIP.2007.891807 [14] Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images, Proceedings of the 6th International Conference on Computer Vision, 839–846 (1998). https://doi.org/10. 1109/ICCV.1998.710815 [15] Immerkaer, J.: Fast noise variance estimation, Computer Vision and Image Understanding, 64, 300–302 (1996). https://doi.org/10.1006/cviu.1996.0060 [16] Zhang, D., Zhou, F.: Self-supervised image denoising for real-world images with contextaware transformer, IEEE Access, 11, 14340–14349 (2023). https://doi.org/10.1109/ ACCESS.2023.3243829 | ||
|
آمار تعداد مشاهده مقاله: 5 تعداد دریافت فایل اصل مقاله: 3 |
||