Multiresolution joint bilateral filtering with modified adaptive shrinkage for image denoising
This paper proposes an effective image denoising of gray level images in wavelet transform domain using joint bilateral filter and modified adaptive shrinkage. The input image is first decomposed using 2D-discrete wavelet transform. The multiresolution joint bilateral filtering is then applied to the approximation sub-band of the decomposed image. Flat and edge regions are obtained using the α-map computed from wavelet transform coefficients of LH, HL, and HH bands. Noise is removed in the flat regions by Inner Product method. After removing noise in the flat regions, further noise removal is done in the edge regions using NeighShrink SURE shrinkage functions. The modified wavelet coefficients in the flat and edge regions are combined and filtered by using Gaussian low pass filter. Finally the denoised output image is reconstructed using 2D-inverse discrete wavelet transform. Experimentation has been carried out on set of standard test images using the proposed algorithm and its performance is evaluated and compared with existing state of art methods using PSNR, EKI and Computation time. Experimental results show that the proposed algorithm can effectively reduce noise without losing sharp details in the noisy images and is suitable for commercial low-cost imaging systems.
KeywordsJoint Bilateral filter Image denoising Shrinkage functions Wavelet transform
The authors are pledging plentiful of thanks and gratefulness to the reviewers for their valuable comments.
- 2.Arivazhagan S, Sugitha N, Vijay A (2015) A novel image denoising scheme based on fusing multiresolution and spatial filters, −SIViP. Springer J 9(4):885–892Google Scholar
- 6.Chatterjee P, Milanfar P (2012) Patch-based near-optimal image denoising. IEEE Trans Image Process 21(4)Google Scholar
- 13.Gonzalez R, Woods R (2001) Digital image processing 2nd ed, Prentice HallGoogle Scholar
- 14.Jain P, Tyagi V (2015) An adaptive edge-preserving image denoising technique using tetrolet transforms. Int J Comput Graphics 31(5):657–674Google Scholar
- 15.Kim S, Kang W, Lee E, Paik J (2010) Wavelet-domain color image enhancement using filtered directional bases and frequency-adaptive shrinkage. IEEE Trans Consum Electron 56(2)Google Scholar
- 19.Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A (2009) Non-local sparse models for image restoration in Proc. IEEE Int Conf ComputVis, Kyoto 2272–2279Google Scholar
- 20.Mohideen SK, Perumal SA, Krishnan N, Selvakumar RK (2010) A novel approach for image denoising using dynamic tracking with new threshold technique , IEEE Int Conf Comput Intell Comput Res 1–4Google Scholar
- 23.Petschnigg G, Agrawala M, Hoppe H, Szeliski R, Cohen M, Toyama K (2004) Digital photography with flash and no-flash image pairs. In: proceedings of SIGGRAPH, 664–672Google Scholar
- 28.Sudha S, Suresh GR, Sukanesh R (2007) Wavelet based image denoising using adaptive thresholding. Int Conf Comput Int Mult Appl 3:296–300Google Scholar
- 31.Zhang M, Gunturk BK (2008) Multiresolution bilateral filtering for image denoisin. IEEE Trans Image Process 17(12)Google Scholar