Improved image denoising via RAISR with fewer filters

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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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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Correspondence to Masaaki Ikehara.

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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 (2021). https://doi.org/10.1007/s41095-021-0213-0

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Keywords

  • block matching and 3D filtering
  • weighted nuclear norm minimization
  • super-resolution
  • geometric conversion
  • census transform