|
|
基于PMP和SSR的图像去块算法
|
Abstract:
块离散余弦变换(BDCT)编码在图像和视频压缩领域应用广泛。然而，在低码率编码条件下，压缩图像的块边缘常会出现明显的块效应，这极大地影响了图像的视觉效果。为此，本文提出了一种结合局部最小像素(PMP)正则化和结构稀疏表示(SSR)的方法，旨在去除压缩图像中的块状伪影，同时保留图像的锐利边缘和细节信息。具体而言，我们利用内部结构稀疏先验来消除图像噪声，并借助外部结构稀疏先验防止图像过拟合。此外，通过实施局部最小像素正则化约束，能够有效区分块化图像和清晰图像，增强块化图像的恢复效果。在处理所提模型的非凸性问题时，我们在交替迭代法中融入了滤波技术。实验结果表明，该算法在客观性和视觉感知方面均达到了与当前几种先进去块算法相当的水平。
Block discrete cosine transform (BDCT) coding is widely used in image and video compression. However, under low bit rate coding conditions, the block edges of compressed images often show obvious block effect, which greatly affects the visual effect of the images. To this end, this paper proposes a method that combines local minimum pixel (PMP) regularisation and structured sparse representation (SSR) with the aim of removing block artefacts from compressed images while preserving the sharp edges and detail information of the images. Specifically, we remove image noise using an internal structural sparse prior and prevent image overfitting with the help of an external structural sparse prior. In addition, by implementing the local minimum pixel regularisation constraint, we are able to effectively distinguish blocked images from clear images and enhance the recovery of blocked images. When dealing with the non-convexity problem of the proposed model, we incorporate the filtering technique in the alternating iteration method. Experimental results show that the algorithm achieves a level comparable to several current state-of-the-art deblocking algorithms in terms of objectivity and visual perception.
| [1] | Shen, M. and Kuo, C.J. (1998) Review of Postprocessing Techniques for Compression Artifact Removal. Journal of Visual Communication and Image Representation, 9, 2-14. https://doi.org/10.1006/jvci.1997.0378 |
| [2] | Reeve III, H.C. and Lim, J.S. (1984) Reduction of Blocking Effects in Image Coding. Optical Engineering, 23, Article 230134. https://doi.org/10.1117/12.7973248 |
| [3] | Foi, A., Katkovnik, V. and Egiazarian, K. (2007) Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images. IEEE Transactions on Image Processing, 16, 1395-1411. https://doi.org/10.1109/tip.2007.891788 |
| [4] | Elvira, V., Miguez, J. and Djurić, P.M. (2021) On the Performance of Particle Filters with Adaptive Number of Particles. Statistics and Computing, 31, Article No. 81. https://doi.org/10.1007/s11222-021-10056-0 |
| [5] | Zhai, G.T., Zhang, W.J., Yang, X.K., Lin, W.S. and Xu, Y. (2008) Efficient Deblocking with Coefficient Regularization, Shape-Adaptive Filtering, and Quantization Constraint. IEEE Transactions on Multimedia, 10, 735-745. https://doi.org/10.1109/tmm.2008.922849 |
| [6] | Kim, J. (2009) Adaptive Blocking Artifact Reduction Using Wavelet-Based Block Analysis. IEEE Transactions on Consumer Electronics, 55, 933-940. https://doi.org/10.1109/tce.2009.5174477 |
| [7] | Nath, V.K., Baruah, H.G. and Hazarika, D. (2018) An Image Deblocking Approach Based on Non-Subsampled Shearlet Transform. In: Advances in Intelligent Systems and Computing, Springer, 231-238. https://doi.org/10.1007/978-981-13-1610-4_24 |
| [8] | Kaziakhmedov, E., Yousfi, Y., Dworetzky, E. and Fridrich, J. (2023) Cost Polarization by Dequantizing for JPEG Steganography. Electronic Imaging, 35, 374-1-374-14. https://doi.org/10.2352/ei.2023.35.4.mwsf-374 |
| [9] | Lebrun, M., Buades, A. and Morel, J.M. (2013) A Nonlocal Bayesian Image Denoising Algorithm. SIAM Journal on Imaging Sciences, 6, 1665-1688. https://doi.org/10.1137/120874989 |
| [10] | Sulistyawati, D.H. and Utomo, H.S. (2016) Perbaikan Citra Dengan Noise Missing Block Menggunakan Implementasi Algoritma Projection onto Convex Sets (POCS). Konvergensi, 11, 11-19. |
| [11] | Shah, D., Zaveri, T., Trivedi, Y.N. and Plaza, A. (2020) Entropy-Based Convex Set Optimization for Spatial-Spectral Endmember Extraction from Hyperspectral Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4200-4213. https://doi.org/10.1109/jstars.2020.3008939 |
| [12] | Huang, J., Ma, J., Liu, N., Zhang, H., Bian, Z., Feng, Y., et al. (2011) Sparse Angular CT Reconstruction Using Non-Local Means Based Iterative-Correction POCS. Computers in Biology and Medicine, 41, 195-205. https://doi.org/10.1016/j.compbiomed.2011.01.009 |
| [13] | Wang, Y., Wang, L., Liu, B. and Zhao, H. (2021) Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory. Sensors, 21, Article 4109. https://doi.org/10.3390/s21124109 |
| [14] | Bredies, K. and Holler, M. (2015) A TGV-Based Framework for Variational Image Decompression, Zooming, and Reconstruction. Part I: Analytics. SIAM Journal on Imaging Sciences, 8, 2814-2850. https://doi.org/10.1137/15m1023865 |
| [15] | Chien, T.V., Dinh, K.Q., Jeon, B. and Burger, M. (2017) Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-Based Sparse Representation. Signal Processing: Image Communication, 54, 93-106. https://doi.org/10.1016/j.image.2017.02.012 |
| [16] | Chambolle, A. and Pock, T. (2021) Learning Consistent Discretizations of the Total Variation. SIAM Journal on Imaging Sciences, 14, 778-813. https://doi.org/10.1137/20m1377199 |
| [17] | Yuan, W., Liu, H., Liang, L., Xie, G., Zhang, Y. and Liu, D. (2023) Rank Minimization via Adaptive Hybrid Norm for Image Restoration. Signal Processing, 206, Article 108926. https://doi.org/10.1016/j.sigpro.2022.108926 |
| [18] | Altantawy, D.A., Saleh, A.I. and Kishk, S.S. (2018) Texture-Guided Depth Upsampling Using Bregman Split: A Clustering Graph-Based Approach. The Visual Computer, 36, 333-359. https://doi.org/10.1007/s00371-018-1611-x |
| [19] | Li, Z., Lv, H., Cheng, L. and Jia, X. (2024) Image Deblocking Algorithm Based on GC and SSR. The Visual Computer, 41, 53-66. https://doi.org/10.1007/s00371-024-03309-2 |
| [20] | Koshelev, I. and Lefkimmiatis, S. (2023) Iterative Reweighted Least Squares Networks with Convergence Guarantees for Solving Inverse Imaging Problems. |
| [21] | Zha, Z., Zhang, X., Wang, Q., Tang, L. and Liu, X. (2018) Group-Based Sparse Representation for Image Compressive Sensing Reconstruction with Non-Convex Regularization. Neurocomputing, 296, 55-63. https://doi.org/10.1016/j.neucom.2018.03.027 |
| [22] | Wang, W., Liu, H. and Xie, G. (2021) Pansharpening of Worldview-2 Data via Graph Regularized Sparse Coding and Adaptive Coupled Dictionary. Sensors, 21, Article 3586. https://doi.org/10.3390/s21113586 |
| [23] | Ismail, I., Meselhy Eltoukhy, M. and Eltaweel, G. (2023) Super-Resolution Based on Curvelet Transform and Sparse Representation. Computer Systems Science and Engineering, 45, 167-181. https://doi.org/10.32604/csse.2023.028906 |
| [24] | Roscher, R. and Waske, B. (2016) Shapelet-Based Sparse Representation for Landcover Classification of Hyperspectral Images. IEEE Transactions on Geoscience and Remote Sensing, 54, 1623-1634. https://doi.org/10.1109/tgrs.2015.2484619 |
| [25] | Dabov, K., Foi, A., Katkovnik, V. and Egiazarian, K. (2007) Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering. IEEE Transactions on Image Processing, 16, 2080-2095. https://doi.org/10.1109/tip.2007.901238 |
| [26] | Mairal, J., Bach, F., Ponce, J., Sapiro, G. and Zisserman, A. (2009) Non-Local Sparse Models for Image Restoration. 2009 IEEE 12th International Conference on Computer Vision, Kyoto, 29 September-2 October 2009, 2272-2279. https://doi.org/10.1109/iccv.2009.5459452 |
| [27] | Aharon, M., Elad, M. and Bruckstein, A. (2006) K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation. IEEE Transactions on Signal Processing, 54, 4311-4322. https://doi.org/10.1109/tsp.2006.881199 |
| [28] | Zhao, C., Zhang, J., Ma, S., Fan, X., Zhang, Y. and Gao, W. (2017) Reducing Image Compression Artifacts by Structural Sparse Representation and Quantization Constraint Prior. IEEE Transactions on Circuits and Systems for Video Technology, 27, 2057-2071. https://doi.org/10.1109/tcsvt.2016.2580399 |
| [29] | Zha, Z., Yuan, X., Wen, B., Zhou, J., Zhang, J. and Zhu, C. (2020) From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration. IEEE Transactions on Image Processing, 29, 3254-3269. https://doi.org/10.1109/tip.2019.2958309 |
| [30] | Chang, H., Ng, M.K. and Zeng, T. (2014) Reducing Artifacts in JPEG Decompression via a Learned Dictionary. IEEE Transactions on Signal Processing, 62, 718-728. https://doi.org/10.1109/tsp.2013.2290508 |
| [31] | Zha, Z., Wen, B., Yuan, X., Zhou, J. and Zhu, C. (2021) Image Restoration via Reconciliation of Group Sparsity and Low-Rank Models. IEEE Transactions on Image Processing, 30, 5223-5238. https://doi.org/10.1109/tip.2021.3078329 |
| [32] | Xiao, J., Zhao, R. and Lam, K. (2021) Bayesian Sparse Hierarchical Model for Image Denoising. Signal Processing: Image Communication, 96, Article 116299. https://doi.org/10.1016/j.image.2021.116299 |
| [33] | Zha, Z., Wen, B., Yuan, X., Zhou, J., Zhu, C. and Kot, A.C. (2022) A Hybrid Structural Sparsification Error Model for Image Restoration. IEEE Transactions on Neural Networks and Learning Systems, 33, 4451-4465. https://doi.org/10.1109/tnnls.2021.3057439 |
| [34] | Pan, J., Sun, D., Pfister, H. and Yang, M. (2016) Blind Image Deblurring Using Dark Channel Prior. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 1628-1636. https://doi.org/10.1109/cvpr.2016.180 |
| [35] | Pan, J., Hu, Z., Su, Z. and Yang, M. (2014) Deblurring Text Images via L0-Regularized Intensity and Gradient Prior. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 23-28 June 2014, 2901-2908. https://doi.org/10.1109/cvpr.2014.371 |
| [36] | Chen, L., Fang, F., Wang, T. and Zhang, G. (2019) Blind Image Deblurring with Local Maximum Gradient Prior. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, 15-20 June 2019, 1742-1750. https://doi.org/10.1109/cvpr.2019.00184 |
| [37] | Yan, Y., Ren, W., Guo, Y., Wang, R. and Cao, X. (2017) Image Deblurring via Extreme Channels Prior. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 21-26 July 2017, 6978-6986. https://doi.org/10.1109/cvpr.2017.738 |
| [38] | Martin, D., Fowlkes, C., Tal, D. and Malik, J. (2001) A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. Proceedings Eighth IEEE International Conference on Computer Vision, Vancouver, 7-14 July 2001, 416-423. https://doi.org/10.1109/iccv.2001.937655 |
| [39] | Kim, J., Lee, J.K. and Lee, K.M. (2016) Accurate Image Super-Resolution Using Very Deep Convolutional Networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 1646-1654. https://doi.org/10.1109/cvpr.2016.182 |
