Wavelet coefficients segmentation for locally adaptive threshold image denoising method

doi:10.13474/j.cnki.11-2246.2020.0142

Abstract

Abstract: Aiming at the problem that the square local window of the adaptive wavelet threshold denoising can not well adapt to the edge features of the wavelet coefficients, a new image segmentation-based method is proposed. In this method, multi-scale wavelet decomposition is firstly performed on the noisy image. Then, the different scale wavelet coefficients are segmented by using the SLIC superpixel segmentation technology with which some local blocks with similar statistical characteristics will be obtained. BayesShrink thresholds of the wavelet coefficients in every irregular block are estimated and soft threshold shrinkage is utilized. Finally, a denoised image is acquired by the inverse wavelet transform. In this paper, three standard test images and a hyperspectral image are tested. The experimental results show that the proposed method can better adapt to the edge features of the wavelet coefficients, and can not only get better visual effects, but also achieve a higher level of numerical indicators.

Key words: image denoising, wavelet transform, image segmentation, local adaptation, threshold denoising

CLC Number:

LI Yali, ZHANG Songlin, HAN Jie. Wavelet coefficients segmentation for locally adaptive threshold image denoising method[J]. Bulletin of Surveying and Mapping, 2020, 0(5): 43-46,122.

References

[1] 李旭超,朱善安.小波域图像降噪概述[J].中国图象图形学报,2006,11(9):1201-1209.
[2] 邓大新,林春生,龚沈光,等.基于Neyman-Pearson准则的小波阈值去噪法[J].信号处理,2003, 19(3):281-283.
[3] 王红梅,李言俊,张科.基于平稳小波变换的图像去噪方法[J].红外技术,2006,28(7):404-407.
[4] 刘成云,陈振学,马于涛.自适应阈值的小波图像去噪[J].光电工程,2007,34(6):77-81.
[5] 李霞,罗欣,薛辉,等.小波域隐马尔可夫树模型对多光谱影像的去噪研究[J].测绘科学技术学报,2014, 31(3):274-277,282.
[6] 陈竹安,胡志峰.小波阈值改进算法的遥感图像去噪[J].测绘通报,2018(4):28-31.
[7] 王昶,张永生,王旭,等.遥感影像条带噪声去除的小波变分法[J].测绘学报,2019,48(8):1025-1037.
[8] DONOHO D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3):613-627.
[9] DONOHO D L, JOHNSTONE I M. Adapting to unknown smoothness via wavelet shrinkage[J]. Journal of the American Statistical Association, 1995,90(432):1200-1224.
[10] CHIPMAN H A, KOLACZYK E D, MCCULLOCH R E. Adaptive bayesian wavelet shrinkage[J]. Journal of the American Statistical Association, 1997,92(440):1413-1421.
[11] CHANG S G, YU B, VETTERLI M. Adaptive wavelet thresholding for image denoising and compression[J].IEEE Transactions on Image Processing, 2000,9(9):1532-1546.
[12] PORTILLA J, STRELA V, WAINWRIGHT M J, et al. Image denoising using scale mixtures of gaussians in the wavelet domain[J].IEEE Transactions on Image Processing,2003,12(11):1338-1351.
[13] CHANG S G, YU B, VETTERLI M. Spatially adaptive wavelet thresholding with context modeling for image denoising[J]. IEEE Transactions on Image Processing,2000,9(9): 1522-1531.
[14] JAIN P, TYAGI V. LAPB: Locally adaptive patch-based wavelet domain edge-preserving image denoising[J]. Information Sciences, 2015, 294:164-181.
[15] DONOHO D L, JOHNSTONE I M. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 81(3): 425-455.
[16] ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282.