A New Image Denoising Method Based on Adaptive Multiscale Morphological Edge Detection

Wavelet transform is an effective method for removal of noise from image. But traditional wavelet transform cannot improve the smooth effect and reserve image’s precise details simultaneously; even false Gibbs phenomenon can be produced. This paper proposes a new image denoising method based on adaptive multiscale morphological edge detection beyond the above limitation. Firstly, the noisy image is decomposed by using one wavelet base. Then, the image edge is detected by using the adaptive multiscale morphological edge detection based on the wavelet decomposition. On this basis, wavelet coefficients belonging to the edge position are dealt with with the improved wavelet domain wiener filtering, and the others are dealt with with the improved Bayesian threshold and the improved threshold function. Finally, wavelet coefficients are inversely processed to obtain the denoised image. Experimental results show that this method can effectively remove the image noise without blurring edges and highlight the characteristics of image edge at the same time. The validation results of the denoised images with higher peak signal to noise ratio (PSNR) and structural similarity (SSIM) demonstrate their robust capability for real applications in the future.

[1]  Ke Ma,et al.  Modified BM3D algorithm for image denoising using nonlocal centralization prior , 2015, Signal Process..

[2]  Gitam Shikkenawis,et al.  Image denoising using orthogonal locality preserving projections , 2015, J. Electronic Imaging.

[3]  Peter J. Ramadge,et al.  Edge-Preserving Image Regularization Based on Morphological Wavelets and Dyadic Trees , 2012, IEEE Transactions on Image Processing.

[4]  Gang Wang,et al.  Image denoising based on edge detection and prethresholding Wiener filtering of multi-wavelets fusion , 2015, Int. J. Wavelets Multiresolution Inf. Process..

[5]  Amit Phadikar,et al.  Image Error concealment Based on QIM Data Hiding in Dual-Tree Complex Wavelets , 2012, Int. J. Wavelets Multiresolution Inf. Process..

[6]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.

[7]  Saeid Saryazdi,et al.  Efficient diffusion coefficient for image denoising , 2016, Comput. Math. Appl..

[8]  Tien D. Bui,et al.  Fast image enhancement in compressed wavelet domain , 2014, Signal Process..

[9]  Eunsung Lee,et al.  Wavelet-domain color image enhancement using filtered directional bases and frequency-adaptive shrinkage , 2010, IEEE Transactions on Consumer Electronics.

[10]  Ling Shao,et al.  Nonlocal Hierarchical Dictionary Learning Using Wavelets for Image Denoising , 2013, IEEE Transactions on Image Processing.

[11]  Yang Su,et al.  Parallel implementation of wavelet-based image denoising on programmable PC-grade graphics hardware , 2010, Signal Process..

[12]  Yan Shi,et al.  Translation Invariant Directional Framelet Transform Combined With Gabor Filters for Image Denoising , 2014, IEEE Transactions on Image Processing.

[13]  Liangcai Cao,et al.  Image denoising with anisotropic bivariate shrinkage , 2011, Signal Process..

[14]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[15]  Qi Li,et al.  A novel method of infrared image denoising and edge enhancement , 2008, Signal Process..

[16]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

[17]  Vittoria Bruni,et al.  Wavelet-based signal de-noising via simple singularities approximation , 2006, Signal Process..

[18]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[19]  Salah Bourennane,et al.  Denoising and Dimensionality Reduction Using Multilinear Tools for Hyperspectral Images , 2008, IEEE Geoscience and Remote Sensing Letters.

[20]  Florence Tupin,et al.  NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Soontorn Oraintara,et al.  Microarray Image Denoising Using Complex Gaussian Scale Mixtures of Complex Wavelets , 2014, IEEE Journal of Biomedical and Health Informatics.

[22]  Michael Elad,et al.  Dictionary Learning for Analysis-Synthesis Thresholding , 2014, IEEE Transactions on Signal Processing.

[23]  Yide Ma,et al.  Image denoising via bivariate shrinkage function based on a new structure of dual contourlet transform , 2015, Signal Process..

[24]  Dennis M. Healy,et al.  Wavelet transform domain filters: a spatially selective noise filtration technique , 1994, IEEE Trans. Image Process..

[25]  Wen-Liang Hwang,et al.  Wavelet Bayesian Network Image Denoising , 2013, IEEE Transactions on Image Processing.

[26]  Xiangchu Feng,et al.  A divide-and-conquer stochastic alterable direction image denoising method , 2015, Signal Process..

[27]  David R. Bull,et al.  Dual-tree complex wavelet coefficient magnitude modelling using the bivariate Cauchy-Rayleigh distribution for image denoising , 2014, Signal Process..

[28]  Nelson D. A. Mascarenhas,et al.  Nonlocal Markovian models for image denoising , 2016, J. Electronic Imaging.

[29]  Khalil Ahmad,et al.  Image denoising using local contrast and adaptive mean in wavelet transform domain , 2014, Int. J. Wavelets Multiresolution Inf. Process..