Abstract
We propose dual-domain filtering, an image processing paradigm that couples spatial domain with frequency domain filtering. Our dual-domain defined filter removes artifacts like residual noise of other image denoising methods and compression artifacts. Moreover, iterating the filter achieves state-of-the-art image denoising results, but with a much simpler algorithm than competing approaches. The simplicity and versatility of the dual-domain filter makes it an attractive tool for image processing.
Keywords
MSC codes
Formats available
You can view the full content in the following formats:
References
1.
N. Azzabou, N. Paragios, and F. Guichard, Image denoising based on adapted dictionary computation, in Proceedings of the IEEE International Conference on Image Processing (ICIP 2007), IEEE, Piscataway, NJ, 2007, Vol. 3, pp. III-109--III-112.
2.
P. Bouboulis, K. Slavakis, and S. Theodoridis, Adaptive kernel-based image denoising employing semi-parametric regularization, IEEE Trans. Image Process., 19 (2010), pp. 1465--1479.
3.
A. Buades, B. Coll, and J. M. Morel, A review of image denoising algorithms, with a new one, Multiscale Model. Simul., 4 (2005), pp. 490--530.
4.
H. C. Burger, C. J. Schuler, and S. Harmeling, Image denoising: Can plain neural networks compete with BM3D?, in Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Piscataway, NJ, 2012, pp. 2392--2399.
5.
L. Caraffa, J.-P. Tarel, and P. Charbonnier, The guided bilateral filter: When the joint/cross bilateral filter becomes robust, IEEE Trans. Image Process., 24 (2015), pp. 1199--1208.
6.
P. Chatterjee and P. Milanfar, A generalization of non-local means via kernel regression, in Proc. SPIE 6814, SPIE, Bellingham, WA, 2008, 68140P.
7.
P. Chatterjee and P. Milanfar, Clustering-based denoising with locally learned dictionaries, IEEE Trans. Image Process., 18 (2009), pp. 1438--1451.
8.
P. Chatterjee and P. Milanfar, Patch-based near-optimal image denoising, IEEE Trans. Image Process., 21 (2012), pp. 1635--1649.
9.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image denoising by sparse \textup3-d transform-domain collaborative filtering, IEEE Trans. Image Process., 16 (2007), pp. 2080--2095.
10.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, BM\textup3D image denoising with shape-adaptive principal component analysis, in Proceedings of the Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS'09), 2009.
11.
A. Dauwe, B. Goossens, H. Q. Luong, and W. Philips, A fast non-local image denoising algorithm, in Proc. SPIE 6812, SPIE, Bellingham, WA, 2008, 681210.
12.
W. Dong, G. Shi, and X. Li, Nonlocal image restoration with bilateral variance estimation: A low-rank approach, IEEE Trans. Image Process., 22 (2013), pp. 700--711.
13.
F. Durand and J. Dorsey, Fast bilateral filtering for the display of high-dynamic-range images, ACM Trans. Graphics (TOG), 21 (2002), pp. 257--266.
14.
M. Elad, On the origin of the bilateral filter and ways to improve it, IEEE Trans. Image Process., 11 (2002), pp. 1141--1151.
15.
M. Elad and M. Aharon, Image denoising via sparse and redundant representations over learned dictionaries, IEEE Trans. Image Process., 15 (2006), pp. 3736--3745.
16.
A. Foi, V. Katkovnik, and K. Egiazarian, Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images, IEEE Trans. Image Process., 16 (2007), pp. 1395--1411.
17.
S. Grewenig, S. Zimmer, and J. Weickert, Rotationally invariant similarity measures for nonlocal image denoising, J. Vis. Commun. Image Represent., 22 (2011), pp. 117--130.
18.
K. He, J. Sun, and X. Tang, Guided image filtering, IEEE Trans. Pattern Anal. Machine Intell., 35 (2013), pp. 1397--1409.
19.
C. Kervrann and J. Boulanger, Optimal spatial adaptation for patch-based image denoising, IEEE Trans. Image Process., 15 (2006), pp. 2866--2878.
20.
C. Knaus and M. Zwicker, Dual-domain image denoising, in Proceedings of the 20th IEEE International Conference on Image Processing (ICIP 2013), IEEE, Piscataway, NJ, 2013, pp. 440--444.
21.
C. Knaus and M. Zwicker, Progressive image denoising, IEEE Trans. Image Process., 23 (2014), pp. 3114--3125.
22.
M. Lebrun, A. Buades, and J. M. Morel, A nonlocal Bayesian image denoising algorithm, SIAM J. Imaging Sci., 6 (2013), pp. 1665--1688.
23.
M. Mahmoudi and G. Sapiro, Fast image and video denoising via nonlocal means of similar neighborhoods, IEEE Signal Process. Lett., 12 (2005), pp. 839--842.
24.
J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, Non-local sparse models for image restoration, in Proceedings of the 12th IEEE International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, 2009, pp. 2272--2279.
25.
V. K. Nath, D. Hazarika, and A. Mahanta, Blocking artifacts reduction using adaptive bilateral filtering, in Proceedings of the 2010 International Conference on Signal Processing and Communications (SPCOM), IEEE, Piscataway, NJ, 2010, pp. 1--5.
26.
J. Orchard, M. Ebrahimi, and A. Wong, Efficient nonlocal-means denoising using the SVD, in Proceedings of the 15th IEEE International Conference on Image Processing (ICIP 2008), IEEE Press, Piscataway, NJ, 2008, pp. 1732--1735.
27.
G. Petschnigg, R. Szeliski, M. Agrawala, M. Cohen, H. Hoppe, and K. Toyama, Digital photography with flash and no-flash image pairs, ACM Trans. Graph., 23 (2004), pp. 664--672.
28.
G. Peyré, Image processing with nonlocal spectral bases, Multiscale Model. Simul., 7 (2008), pp. 703--730.
29.
J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, Image denoising using scale mixtures of Gaussians in the wavelet domain, IEEE Trans. Image Process., 12 (2003), pp. 1338--1351.
30.
L. Shao, R. Yan, X. Li, and Y. Liu, From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms, IEEE Trans. Cybernet., 44 (2014), pp. 1001--1013.
31.
H. Takeda, S. Farsiu, and P. Milanfar, Kernel regression for image processing and reconstruction, IEEE Trans. Image Process., 16 (2007), pp. 349--366.
32.
C. Tomasi and R. Manduchi, Bilateral filtering for gray and color images, in Proceedings of the 6th International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, 1998, pp. 839--846.
33.
D. Van De Ville and M. Kocher, SURE-based non-local means, IEEE Signal Process. Lett. 16 (2009), pp. 973--976.
34.
D. Van De Ville and M. Kocher, Nonlocal means with dimensionality reduction and SURE-based parameter selection, IEEE Trans. Image Process., 20 (2011), pp. 2683--2690.
35.
G. Yu and G. Sapiro, DCT image denoising: A simple and effective image denoising algorithm, IPOL Journal. Image Processing On Line, 1 (2011), http://dx.doi.org/10.5201/ipol.2011.ys-dot.
36.
M. Zhang and B. K. Gunturk, Compression artifact reduction with adaptive bilateral filtering, in Visual Communications and Image Processing 2009, Vol. 7257, SPIE, Bellingham, WA, 2009.
37.
Q. Zhang, X. Shen, L. Xu, and J. Jia, Rolling guidance filter, in Proceedings of the 13th European Conference on Computer Vision (ECCV 2014), Lecture Notes in Comput. Sci. 8691, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, eds., Springer, New York, 2014, pp. 815--830.
Information & Authors
Information
Published In
Copyright
© 2015, Society for Industrial and Applied Mathematics. This work is licensed under a Creative Commons Attribution 4.0 International License
History
Submitted: 23 July 2014
Accepted: 30 April 2015
Published online: 8 July 2015
Keywords
MSC codes
Authors
Metrics & Citations
Metrics
Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited By
- Adaptive dual-domain filtering for random seismic noise removalGEOPHYSICS, Vol. 89, No. 5 | 25 July 2024
- Boosting of Implicit Neural Representation-Based Image DenoiserICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) | 14 Apr 2024
- Multi-Scale Based Approach for Denoising Real-World Noisy Image Using Curvelet Thresholding: Scope and BeyondIEEE Access, Vol. 12 | 1 Jan 2024
- Quantitative analysis method for infrared characterization of coal damage under load based on image enhancement and pixel extractionGeomatics, Natural Hazards and Risk, Vol. 14, No. 1 | 25 October 2023
- Efficient dual domain image denoising via SURE-based optimizationInternational Journal of Machine Learning and Cybernetics, Vol. 14, No. 12 | 8 July 2023
- Ultrasonic defect detection of high-density polyethylene pipe materials using FIR filtering and block-wise singular value decompositionUltrasonics, Vol. 134 | 1 Sep 2023
- Compressed sensing based on L1 and TGV regularization for low-light-level images denoisingDigital Signal Processing, Vol. 136 | 1 May 2023
- BTGP: Enhancing the Perceptual Recovery of the Image Compressive Sensing using a Backtracking Greedy Pursuit AlgorithmJournal of King Saud University - Computer and Information Sciences, Vol. 24 | 1 Apr 2023
- Infrared image denoising via adversarial learning with multi-level feature attention networkInfrared Physics & Technology, Vol. 128 | 1 Jan 2023
- Improved BM3D method with modified block-matching and multi-scaled imagesMultimedia Tools and Applications, Vol. 4 | 21 February 2022
- D2Defend: Dual-Domain based Defense against Adversarial Examples2021 International Joint Conference on Neural Networks (IJCNN) | 18 Jul 2021
- Geometrical-Based Generative Adversarial Network to Enhance Digital Rock Image QualityPhysical Review Applied, Vol. 15, No. 6 | 14 June 2021
- Infrared image denoising based on the variance-stabilizing transform and the dual-domain filterDigital Signal Processing, Vol. 113 | 1 Jun 2021
- Image denoising via an adaptive weighted anisotropic diffusionMultidimensional Systems and Signal Processing, Vol. 32, No. 2 | 7 January 2021
- A dual-domain deep lattice network for rapid MRI reconstructionNeurocomputing, Vol. 397 | 1 Jul 2020
- Simultaneous denoising and enhancement for X-ray angiograms by employing spatial-frequency filterOptik, Vol. 208 | 1 Apr 2020
- Image denoising review: From classical to state-of-the-art approachesInformation Fusion, Vol. 55 | 1 Mar 2020
- Adaptive Fast Composite Splitting Algorithm for MR Image ReconstructionMachine Intelligence and Signal Processing | 26 February 2020
- Multipatch Unbiased Distance Non-Local Adaptive Means With Wavelet ShrinkageIEEE Transactions on Image Processing, Vol. 29 | 1 Jan 2020
- Combination of global and nonlocal sparse regularization priors for MR image reconstructionTENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) | 1 Oct 2019
- A Non-Local Dual-Domain Approach to Cartoon and Texture DecompositionIEEE Transactions on Image Processing, Vol. 28, No. 4 | 1 Apr 2019
- Image Restoration Based on Adaptive Dual-Domain FilteringMathematical Problems in Engineering, Vol. 2018 | 10 Oct 2018
- Kernel Wiener filtering model with low-rank approximation for image denoisingInformation Sciences, Vol. 462 | 1 Sep 2018
- Progressive Dual-Domain Filter for Enhancing and Denoising Optical Remote-Sensing ImagesIEEE Geoscience and Remote Sensing Letters, Vol. 15, No. 5 | 1 May 2018
- Sure-Based Dual Domain Image Denoising2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) | 1 Apr 2018
- Sparsity Promoting Adaptive Regularization for Compressed Sensing Parallel MRIIEEE Transactions on Computational Imaging, Vol. 4, No. 1 | 1 Mar 2018
- Foveated nonlocal dual denoising2017 IEEE International Conference on Image Processing (ICIP) | 1 Sep 2017
- A Novel Iterative Thresholding Algorithm Based on Plug-and-Play Priors for Compressive SamplingFuture Internet, Vol. 9, No. 3 | 24 June 2017
View Options
View options
- Access via your Institution
- Questions about how to access this content? Contact SIAM at [email protected].