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An optimized hardware design of a two-dimensional guide filter and its application in image denoising

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Abstract

The two-dimensional guided filter is a linear time-varying filter that can filter the noise and retain the detailed information of the image better. It is widely used in image denoising. However, in its application process, it is necessary to calculate the parameters required by filtering in real time according to the data. The operation process involves complex floating point multiplication and division operations, which leads to the high complexity of hardware implementation and excessive resource overhead. It is also difficult to improve the comprehensive speed of the system, which is not good for real-time signal processing. Aiming at the above problems, based on the theory of guided filtering and taking full advantage of the parallelism of FPGA, we proposed a two-dimensional guided filter hardware optimization system based on FPGA. In the system, first, the original image data are cached in real time to reduce the occupation of other hardware resources. Then, the complex mean and variance operations are split into simple sum operations, which reduces the computational complexity. Finally, the system is designed by using serial and parallel flow operation methods, which effectively improve the operation speed of the system. The results show that the operating frequency of the optimized hardware system is up to 146.953 MHz, which is 28.322 MHz higher than that of the original guided filtering algorithm.

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References

  1. Zhang YZ, Xu TF, Liu ZW et al (2015) Infrared image non-uniformity band correction method based on Savitzky–Golay weighted fitting. Chin Opt 8(1):51–57

    Article  Google Scholar 

  2. Sakai M, Parajuli RK, Kubota Y et al (2020) Improved iterative reconstruction method for Compton imaging using median filter. PLoS ONE 15(3):e0229366

    Article  Google Scholar 

  3. Zhang Z, Han D, Dezert J et al (2018) A new adaptive switching median filter for impulse noise reduction with pre-detection based on evidential reasoning. Signal Process 147:173–189

    Article  Google Scholar 

  4. Routray S, Malla PP, Sharma SK et al (2020) A new image denoising framework using bilateral filtering based non-subsampled shearlet transform. Optik Int J Light Electron Opt 216:164903

    Article  Google Scholar 

  5. Bhargava GU, Gangadharan SV (2020) FPGA implementation of modified recursive box filter-based fast bilateral filter for image denoising. Circuits Syst Signal Process 3:1–20

    Google Scholar 

  6. Arabi H, Zaidi H (2020) Spatially guided nonlocal mean approach for denoising of PET images. Med Phys 47(4):1656–1669

    Article  Google Scholar 

  7. Zhan X, Thomas P et al (2019) Denoising high angular resolution diffusion imaging data by combining singular value decomposition and non-local means filter. J Neurosci Methods 312:105–113

    Article  Google Scholar 

  8. Anantrasirichai N, Nicholson L, Morgan JE et al (2014) Adaptive-weighted bilateral filtering and other pre-processing techniques for optical coherence tomography. Comput Med Imaging Graph 38(6):526–539

    Article  Google Scholar 

  9. Du Y, Zhao J, Yao G (2020) Study of comparing several nonlinear filtering algorithms in carrier-based aircraft positioning. J Phys Conf Ser 1549(5):052

    Article  Google Scholar 

  10. Anam C, Adi K, Sutanto H et al (2020) Noise reduction in CT images using a selective mean filter. J Biomed Phys Eng 10(5):623–634

    Article  Google Scholar 

  11. Wang Y, Wang J, Song X et al (2016) An efficient adaptive fuzzy switching weighted mean filter for salt-and-pepper noise removal. IEEE Signal Process Lett 23(11):1582–1586

    Article  Google Scholar 

  12. Serdar E, Ugur E, Samet M (2019) Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal. Multimed Tools Appl 78(24):35401–35418

    Article  Google Scholar 

  13. Feng CL, Zhao DZ, Huang M (2016) Image segmentation using CUDA accelerated non-local means denoising and bias correction embedded fuzzy c-means (BCEFCM). Signal Process 122(C):164–189

    Article  Google Scholar 

  14. Sing JK, Adhikari SK, Basu DK (2015) A modified fuzzy C-means algorithm using scale control spatial information for MRI image segmentation in the presence of noise. J Chemom 29(9):492–505

    Article  Google Scholar 

  15. Zhu SJ, Yu ZK (2019) Self-guided filter for image denoising. IET Image Proc 14(11):2561–2566

    Article  Google Scholar 

  16. Yang AP, Wang HX, Wang JB et al (2018) Single image defogging based on transmittance fusion and multi-directional filtering. Acta Opt Sin 12:112–122

    Google Scholar 

  17. Long BY, Li K, Lu FJ et al (2019) Low-dose CT image processing based on improved guided filtering algorithm. Acta Electron Sin 07:1490–1496

    Google Scholar 

  18. Guo L, Chen L, Wu Y, et al (2016) Image guided fuzzy C-means for image segmentation. In: 2016 International Conference on Fuzzy Theory and its Applications (iFuzzy). IEEE

  19. Jin S (2010) FPGA design and implementation of a real-time stereo vision system. IEEE Trans Circuits Syst Video Technol 20(1):15–26

    Article  Google Scholar 

  20. Ambrosch K, Kubinger W (2010) Accurate hardware-based stereo vision. Comput Vis Image Underst 114(11):1303–1316

    Article  Google Scholar 

  21. Ttofis C, Kyrkou CS, Theocharides T (2015) A hardware-efficient architecture for accurate real-time disparity map estimation. ACM Trans Embedded Comput Syst (TECS)

  22. Wang J, Wang SJ, Hou G (2020) Design of embedded digital image processing system based on Zynq. Lab Sci 2020(23):76–79+85

  23. Nie Y, Wang BW, Wang YP (2020) Design of zynq SOC embedded image edge detection system. Technol Innov Appl 23:54–56

    Google Scholar 

  24. Xu BW, Wang M, Li C et al (2019) Hardware implementation of guided filtering algorithm based on image denoising. Microelectron Comput 36(07):22–26

    Google Scholar 

  25. Ohata K, Sanada Y, Ogaki T et al (2013) Hardware-oriented stereo vision algorithm based on 1-D guided filtering and its FPGA implementation. In: 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS), pp 169–172

  26. Rong SH, Zhou HX, Wen ZG et al (2017) An improved non-uniformity correction algorithm and its hardware implementation on FPGA. Infrared Phys Technol 85:410–420

    Article  Google Scholar 

  27. Vala CK, Immadisetty K, Acharyya A et al (2017) High-speed low-complexity guided image filtering-based disparity estimation. IEEE Trans Circuits Syst I Regular Pap 1–12

  28. Ren GH, Wang GY, Jin YS et al (2013) Design of high-performance wizard filter using FPGA. Infrared Laser Eng 42(2):537–542

    Google Scholar 

  29. He KM, Sun J, Tang XO (2009) Single image haze removal using dark channel prior. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009, CVPR 2009. IEEE, pp 1956–1963

  30. Lou WQ, Wang C, Guan L et al (2020) Neural network instruction set expansion and code mapping mechanism. J Softw 31(10):3074–3086

    Google Scholar 

  31. Li N, Deng JX, Cui YN et al (2020) Infrared image sharpening and FPGA implementation based on dark channel prior. Infrared Laser Eng

  32. Huang HY, Huang SY (2020) Fast hole filling for view synthesis in free viewpoint video. Electronics 9(6):906

    Article  Google Scholar 

  33. Liang TQ, Zhang XY, Duan P et al (2020) Improved dark channel method for underwater target detection with strong scattering media. Infrared Laser Eng 02:104–109

    Google Scholar 

  34. Pashaei M, Starek MJ, Kamangir H et al (2020) Deep learning-based single image super-resolution: an investigation for dense scene reconstruction with UAS photogrammetry. Remote Sens 12(11):1757

    Article  Google Scholar 

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Authors and Affiliations

  1. Hainan University, Haikou, Hainan, China

    Xin Tang, Wenjin Liu, Jia Ren, Yukuan Du & Baodan Chen

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  1. Xin Tang
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  2. Wenjin Liu
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  3. Jia Ren
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  4. Yukuan Du
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  5. Baodan Chen
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Correspondence to Baodan Chen.

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Tang, X., Liu, W., Ren, J. et al. An optimized hardware design of a two-dimensional guide filter and its application in image denoising. J Supercomput 78, 8445–8466 (2022). https://doi.org/10.1007/s11227-021-04044-4

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