Abstract
In recent years, accurate Gaussian noise removal has attracted considerable attention for mobile applications, as in smart phones. Accurate conventional denoising methods have the potential ability to improve denoising performance with no additional time. Therefore, we propose a rapid post-processing method for Gaussian noise removal in this paper. Block matching and 3D filtering and weighted nuclear norm minimization are utilized to suppress noise. Although these nonlocal image denoising methods have quantitatively high performance, some fine image details are lacking due to the loss of high frequency information. To tackle this problem, an improvement to the pioneering RAISR approach (rapid and accurate image super-resolution), is applied to rapidly post-process the denoised image. It gives performance comparable to state-of-the-art super-resolution techniques at low computational cost, preserving important image structures well. Our modification is to reduce the hash classes for the patches extracted from the denoised image and the pixels from the ground truth to 18 filters by two improvements: geometric conversion and reduction of the strength classes. In addition, following RAISR, the census transform is exploited by blending the image processed by noise removal methods with the filtered one to achieve artifact-free results. Experimental results demonstrate that higher quality and more pleasant visual results can be achieved than by other methods, efficiently and with low memory requirements.
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References
Gonzalez, R. C.; Woods, R. E. Digital Image Processing, 3rd edn. Prentice-Hall, Inc., 2006.
Pitas, I.; Venetsanopoulos, A. N. Nonlinear Digitalfilters: Principles and Applications. Springer US, 1990.
Yang, R. K.; Yin, L.; Gabbouj, M.; Astola, J.; Neuvo, Y. Optimal weighted median filtering under structural constraints. IEEE Transactions on Signal Processing Vol. 43, No. 3, 591–604, 1995.
Tomasi, C.; Manduchi, R. Bilateral filtering for gray and color images. In: Proceedings of the 6th International Conference on Computer Vision, 839–846, 1998.
Buades, A.; Coll, B.; Morel, J. M. A non-local algorithm for image denoising. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 60–65, 2005.
Dabov, K.; Foi, A.; Katkovnik, V.; Egiazarian, K. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on Image Processing Vol. 16, No. 8, 2080–2095, 2007.
Gu, S. H.; Zhang, L.; Zuo, W. M.; Feng, X. C. Weighted nuclear norm minimization with application to image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2862–2869, 2014.
Timofte, R.; De, V.; Gool, L. V. Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision, 1920–1927, 2013.
Timofte, R.; de Smet, V.; van Gool, L. A+: Adjusted anchored neighborhood regression for fast superresolution. In: Computer Vision — ACCV 2014. Lecture Notes in Computer Science, Vol. 9006. Cremers, D.; Reid, I.; Saito, H.; Yang, M. H. Eds. Springer Cham, 111–126, 2015.
Yang, C. Y.; Yang, M. H. Fast direct super-resolution by simple functions. In: Proceedings of the IEEE International Conference on Computer Vision, 561–568, 2013.
Dong, C.; Loy, C. C.; He, K. M.; Tang, X. O. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 38, No. 2, 295–307, 2016.
Romano, Y.; Isidoro, J.; Milanfar, P. RAISR: Rapid and accurate image super resolution. IEEE Transactions on Computational Imaging Vol. 3, No. 1, 110–125, 2017.
Jeong, S. C.; Song, B. C. Training-based superresolution algorithm using k-means clustering and detail enhancement. In: Proceedings of the 18th European Signal Processing Conference, 1791–1795, 2010.
Yu, G. S.; Sapiro, G.; Mallat, S. Solving inverse problems with piecewise linear estimators: From Gaussian mixture models to structured sparsity. IEEE Transactions on Image Processing Vol. 21, No. 5, 2481–2499, 2012.
Papyan, V.; Elad, M. Multi-scale patch-based image restoration. IEEE Transactions on Image Processing Vol. 25, No. 1, 249–261, 2016.
Zabih, R.; Woodfill, J. Non-parametric local transforms for computing visual correspondence. In: Computer Vision — ECCV’ 94. Lecture Notes in Computer Science, Vol. 801. Eklundh, J. O. Ed. Springer Berlin Heidelberg, 151–158, 1994.
Feng, X. G.; Milanfar, P. Multiscale principal components analysis for image local orientation estimation. In: Proceedings of the 36th Asilomar Conference on Signals, Systems and Computers, 478–482, 2002.
Bevilacqua, M.; Roumy, A.; Guillemot, C.; Morel, M. L. A. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of the British Machine Vision Conference, 135.1–135.10, 2012.
Acknowledgements
The authors give heartfelt thanks to the Japan International Cooperation Agency (JICA) Project for ASEAN University Network/Southeast Asia Engineering Education Development Network (AUN/SEED) Net, and a Keio Leading-edge Laboratory of Science and Technology (KLL) Ph.D. Program Research Grant for financially supporting this research.
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Theingi Zin received her B.E. degree in electronic engineering from Technological University (Myitkyina), Myitkyina, Myanmar, in 2007. She received her M.E. degree in electrical engineering from Chulalongkorn University, Thailand, in 2012. She is currently a Ph.D. student at Keio University, Yokohama, Japan, under the supervision of Prof. Masaaki Ikehara. Her research interests are in the field of image restoration.
Yusuke Nakahara received his B.E., and M.E degrees in electrical engineering from Keio University, Yokohama, Japan, in 2018 and 2020, respectively. His research interests are in the fields of image super resolution and image denoising.
Takuro Yamaguchi received his B.E., M.E., and Ph.D. degrees in electrical engineering from Keio University, Yokohama, Japan, in 2014, 2016, and 2018, respectively. In 2019, he joined the Faculty of Engineering, Keio University and is currently a research associate with the Department of Electronics and Electrical Engineering, Keio University. His research interests are in the field of image reconstruction.
Masaaki Ikehara received his B.E., M.E. and Dr.Eng. degrees in electrical engineering from Keio University, in 1984, 1986, and 1989, respectively. He was Appointed Lecturer at Nagasaki University, Japan, from 1989 to 1992. In 1992, he joined the Faculty of Engineering, Keio University. From 1996 to 1998, he was a visiting researcher at the University of Wisconsin, Madison, and Boston University, MA. He is currently a full professor with the Department of Electronics and Electrical Engineering, Keio University. His research interests are in the areas of multi-rate signal processing, wavelet image coding, and filter design problems.
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Zin, T., Nakahara, Y., Yamaguchi, T. et al. Improved image denoising via RAISR with fewer filters. Comp. Visual Media 7, 499–511 (2021). https://doi.org/10.1007/s41095-021-0213-0
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DOI: https://doi.org/10.1007/s41095-021-0213-0