{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T05:12:27Z","timestamp":1741237947457,"version":"3.38.0"},"reference-count":28,"publisher":"National Library of Serbia","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>Image denoising remains a fundamental challenge in digital image processing due to the inevitable presence of noise during image acquisition and transmission. While existing noise filtering methods predominantly focus on local spatial information, they often overlook crucial structural information from other perspectives, such as local manifold and global structures. To address this limitation, we propose a novel linear projection-based noise filtering (LPNF) framework grounded in linear projection learning theory. This framework innovatively learns a linear projection for noise filtering by incorporating multiple structural information sources - local spatial, local manifold, and global structures - through well-defined criteria. We present two specialized implementations of the LPNF framework: PCA-based LPNF (LPNF-PCA) and LPP-based LPNF (LPNF-LPP). The LPNF-PCA simultaneously leverages local spatial and global information, while LPNF-LPP integrates both local manifold and spatial information for enhanced denoising performance. Comprehensive experiments conducted on four standard test images with various noise types demonstrate that both LPNF-PCA and LPNF-LPP consistently outperform state-of-the-art denoising methods in terms of both quantitative.<\/jats:p>","DOI":"10.2298\/csis241107010c","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T12:43:27Z","timestamp":1738673007000},"page":"311-325","source":"Crossref","is-referenced-by-count":0,"title":["Linear projection-based noise filtering framework for image denoising"],"prefix":"10.2298","volume":"22","author":[{"given":"Congyin","family":"Cao","sequence":"first","affiliation":[{"name":"School of Information and Intelligence, Hunan Agricultural University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangjun","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Information and Intelligence, Hunan Agricultural University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Menglong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information and Intelligence, Hunan Agricultural University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinghui","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information and Intelligence, Hunan Agricultural University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Aswathy, C., Sowmya, V., Soman, K.: Hyperspectral image denoising using low pass sparse banded filter matrix for improved sparsity based classification. Procedia Computer Science 58, 26-33 (2015)","DOI":"10.1016\/j.procs.2015.08.005"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Gao, Z., Gu, C., Yang, J., Gao, S., Zhong, Y.: Random weighting-based nonlinear gaussian filtering. IEEE Access 8, 19590-19605 (2020)","DOI":"10.1109\/ACCESS.2020.2968363"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Gholami Bahador, F., Mokhtary, P., Lakestani, M.: Mixed poisson-gaussian noise reduction using a time-space fractional differential equations. Information Sciences 647, 119417 (2023)","DOI":"10.1016\/j.ins.2023.119417"},{"key":"ref4","unstructured":"Gonzalez, R.C., Woods, R.E.: Digital Image Processing (3rd Edition). Pearson Education (2008)"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Goyal, B., Dogra, A., Agrawal, S., Sohi, B.S., Sharma, A.: Image denoising review: From classical to state-of-the-art approaches. Information fusion 55, 220-244 (2020)","DOI":"10.1016\/j.inffus.2019.09.003"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"He, L., Zhang, Q., Yang, X.,Wang, Y.,Wang, C.: Sln-red: Regularization by simultaneous local and nonlocal denoising for image restoration. IEEE Signal Processing Letters (2023)","DOI":"10.1109\/LSP.2023.3265174"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Hotelling, H.: Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24(6), 417-441 (1933)","DOI":"10.1037\/h0071325"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Huang, J., Jin, Z.: Enhanced image denoising with diffusion probability and dictionary learning adaptation. Technical gazette 31(3), 774-783 (2024)","DOI":"10.17559\/TV-20230808000859"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Huang, T.S., Yang, G.J., Tang, G.Y.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing 27(1), 13-18 (1997)","DOI":"10.1109\/TASSP.1979.1163188"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Jebur, R.S., Der, C.S., Hammood, D.A.,Weng, L.Y.: Image denoising techniques: An overview. AIP Conference Proceedings 2804(1), 020002 (09 2023)","DOI":"10.1063\/5.0154497"},{"key":"ref11","unstructured":"Jolliffe, I.T.: Principal Component Analysis (2nd Edition). Springer (2002)"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Liu, J., Sun, H., Wu, M., Yang, J.: Gpu implementation of a fast multichannel wiener filter algorithm for active noise control. IEEE Signal Processing Letters (2024)","DOI":"10.1109\/LSP.2024.3380448"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Mafi, M., Martin, H., Cabrerizo, M., Andrian, J., Barreto, A., Adjouadi, M.: A comprehensive survey on impulse and gaussian denoising filters for digital images. Signal Processing 157, 236-260 (2019)","DOI":"10.1016\/j.sigpro.2018.12.006"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Pearson, K.: On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2(11), 559-572 (1901)","DOI":"10.1080\/14786440109462720"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Saadia, A., Rashdi, A.: A speckle noise removal method. Circuits, Systems, and Signal Processing 37, 2639-2650 (2018)","DOI":"10.1007\/s00034-017-0687-2"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., Smola, A., M\u00fcller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5), 1299-1319 (1997)","DOI":"10.1162\/089976698300017467"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Selesnick, I.W., Graber, H.L., Pfeil, D.S., Barbour, R.L.: Simultaneous low-pass filtering and total variation denoising. IEEE Transactions on Signal Processing 62(5), 1109-1124 (2014)","DOI":"10.1109\/TSP.2014.2298836"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Susladkar, O., Deshmukh, G., Nag, S., Mantravadi, A., Makwana, D., Ravichandran, S., Chavhan, G.H., Mohan, C.K., Mittal, S., et al.: Clarifynet: A high-pass and low-pass filtering based cnn for single image dehazing. Journal of systems architecture 132, 102736 (2022)","DOI":"10.1016\/j.sysarc.2022.102736"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Tang, J., Wang, Y., Cao, W., Yang, J.: Improved adaptive median filtering for structured light image denoising. In: 2019 7th International conference on information, communication and networks (ICICN). pp. 146-149. IEEE (2019)","DOI":"10.1109\/ICICN.2019.8834974"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Thanh, D., Surya, P., et al.: A review on ct and x-ray images denoising methods. Informatica 43(2) (2019)","DOI":"10.31449\/inf.v43i2.2179"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Thanh, D.N., Hien, N.N., Kalavathi, P., Prasath, V.S.: Adaptive switching weight mean filter for salt and pepper image denoising. Procedia Computer Science 171, 292-301 (2020)","DOI":"10.1016\/j.procs.2020.04.031"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision (ICCV). pp. 839-846. IEEE, Bombay, India (1998)","DOI":"10.1109\/ICCV.1998.710815"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Uddin, M.P., Mamun, M.A., Hossain, M.A.: Pca-based feature reduction for hyperspectral remote sensing image classification. IETE Technical Review 38(4), 377-396 (2021)","DOI":"10.1080\/02564602.2020.1740615"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Wang, J., Wu, Y., Li, B., Yang, Z., Nie, F.: Fast anchor graph preserving projections. Pattern Recognition 146, 109996 (2024)","DOI":"10.1016\/j.patcog.2023.109996"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Yang, R., Li, D.: Adaptive wavelet transform based on artificial fish swarm optimization and fuzzy c-means method for noisy image segmentation. Computer Science and Information Systems 19(3), 1389-1408 (2022)","DOI":"10.2298\/CSIS220321039Y"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, Z.y.,Wang, L.z., Chen, Y.p.: Salt-and-pepper denoising method for colour images based on tensor low-rank prior and implicit regularization. IET Image Processing 17(3), 886- 900 (2023)","DOI":"10.1049\/ipr2.12680"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Zhang, X.: Center pixel weight based on wiener filter for non-local means image denoising. Optik 244, 167557 (2021)","DOI":"10.1016\/j.ijleo.2021.167557"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Zhang, X., Liao, H., Du, X., Xu, B.: A fast hybrid noise filtering algorithm based on medianmean. In: 2018 IEEE International Conference on Mechatronics and Automation (ICMA). pp. 2120-2125. IEEE (2018)","DOI":"10.1109\/ICMA.2018.8484392"}],"container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T09:27:32Z","timestamp":1741166852000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02142500010C"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.2298\/csis241107010c","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"type":"print","value":"1820-0214"},{"type":"electronic","value":"2406-1018"}],"subject":[],"published":{"date-parts":[[2025]]}}}