Image Denoising Method Based on 3D Block Matching with Harmonic Filtering in Transform Domain
DOI:
https://doi.org/10.34010/injiiscom.v7i1.15615Keywords:
Image denoising, Diffusion model, BM3D, Laplacian-Gaussian algorithmAbstract
Digital images are highly susceptible to noise during acquisition and transmission, requiring efficient preprocessing methods. Traditional denoising techniques often suffer from high computational complexity. To address this challenge, this paper presents a novel and efficient denoising algorithm that integrates wave-domain harmonic filtering with 3D block matching (BM3D). Similar 2D image blocks are grouped into a 3D array using the Euclidean distance approach for joint filtering. Following inverse transformation, wavelet decomposition isolates high-frequency noise. To prevent edge blurring and distortion, a Laplacian-Gaussian algorithm is incorporated to refine the diffusion model. Experimental results demonstrate that the proposed model significantly improves information protection, edge preservation, and computational speed
References
Buades, A., Coll, B., & Morel, J. M. (2005, June). A non-local algorithm for image denoising. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) (Vol. 2, pp. 60-65). Ieee.
Cornelis, B., Moonen, M., & Wouters, J. (2014). Reduced-bandwidth multi-channel Wiener filter based binaural noise reduction and localization cue preservation in binaural hearing aids. Signal processing, 99, 1-16.
Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007). Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on image processing, 16(8), 2080-2095.
Elad, M., Kawar, B., & Vaksman, G. (2023). Image denoising: The deep learning revolution and beyond—a survey paper. SIAM Journal on Imaging Sciences, 16(3), 1594-1654.
Elmoataz, A., Desquesnes, X., & Lézoray, O. (2012). Non-local morphological PDEs and $ p $-Laplacian equation on graphs with applications in image processing and machine learning. IEEE Journal of Selected Topics in Signal Processing, 6(7), 764-779.
Fan, L., Zhang, F., Fan, H., & Zhang, C. (2019). Brief review of image denoising techniques. Visual computing for industry, biomedicine, and art, 2(1), 7.
Foi, A., Katkovnik, V., & Egiazarian, K. (2007). Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images. IEEE transactions on image processing, 16(5), 1395-1411.
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786), 504-507.
Hwang, H., & Haddad, R. A. (1995). Adaptive median filters: new algorithms and results. IEEE Transactions on image processing, 4(4), 499-502.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
LeCun, Y. (1989). Generalization and network design strategies. Connectionism in perspective, 19(143-155), 18.
LI, W. G., & ZHAO, X. M. (2013). Multi-wavelet image denoising based on artifical bee colony algorithm. Journal of Chongqing University of Posts and Telecommunication (Natural Science Edition), 25(4), 532-537.
Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE transactions on pattern analysis and machine intelligence, 11(7), 674-693.
Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), 629-639.
Toprak, A., & Güler, İ. (2006). Suppression of impulse noise in medical images with the use of fuzzy adaptive median filter. Journal of Medical systems, 30, 465-471.
Xian-Chun, Z., Mei-Ling, W., Lan-Fang, S., & Lin-Feng, Z. (2015). Diffusion denoising model based on the wavelet and biharmonic equation. Acta Physica Sinica, 64(6).
Xie, J., Xu, L., & Chen, E. (2012). Image denoising and inpainting with deep neural networks. Advances in neural information processing systems, 25.
Yuan, L., & Xu, X. (2015, August). Adaptive image edge detection algorithm based on canny operator. In 2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS) (pp. 28-31). IEEE.
